From 018c2479af7cec8359a89a387b48f7632e9866a8 Mon Sep 17 00:00:00 2001 From: hussein Date: Thu, 11 Dec 2025 10:37:28 -0500 Subject: [PATCH 1/8] Content Embeddings MultiheadMLP --- pyproject.toml | 2 + .../cc-scripts/wet_extract_domain_content.py | 21 +- tgrag/content_embbeding/TFIDF.py | 70 + tgrag/content_embbeding/llm_embedding.py | 36 + tgrag/experiments/mlp_experiments/main.py | 623 ++- .../mlp_experiments/mlp_multihead.py | 109 + uv.lock | 4141 +++++++++-------- 7 files changed, 2823 insertions(+), 2179 deletions(-) create mode 100644 tgrag/content_embbeding/TFIDF.py create mode 100644 tgrag/content_embbeding/llm_embedding.py create mode 100644 tgrag/experiments/mlp_experiments/mlp_multihead.py diff --git a/pyproject.toml b/pyproject.toml index 64d79b58..7c2550f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,8 @@ dependencies = [ "cloudscraper>=1.2.71", "BeautifulSoup4>=4.13.0", "jsonpath-ng>=1.7.0", + "ollama>=0.5.0", + "nltk", ] [tool.flit.module] diff --git a/tgrag/cc-scripts/wet_extract_domain_content.py b/tgrag/cc-scripts/wet_extract_domain_content.py index 23459325..996fa5e2 100644 --- a/tgrag/cc-scripts/wet_extract_domain_content.py +++ b/tgrag/cc-scripts/wet_extract_domain_content.py @@ -7,10 +7,10 @@ import os import shutil import requests - from pyspark.sql import Window from pyspark.sql.functions import row_number from pyspark.sql import functions as F +from pyspark.sql.functions import input_file_name class ExtractWetContentsJob(CCSparkJob): """Extract links from WAT files and redirects from WARC files and save them as pairs . @@ -19,7 +19,8 @@ class ExtractWetContentsJob(CCSparkJob): num_output_partitions = 4 name = 'ExtractWetContentsJob' output_schema = StructType( - [StructField('Domain_Name', StringType(), True), + [StructField('FileName', StringType(), True), + StructField('Domain_Name', StringType(), True), StructField('WARC_Target_URI', StringType(), True), StructField('WARC_Identified_Content_Language', StringType(), True), StructField('WARC_Date', StringType(), True), @@ -51,12 +52,13 @@ def _url_join(base, link): def iterate_records(self, warc_uri, archive_iterator): count=0 + file_name=warc_uri.split("/segments/")[-1] for record in archive_iterator: - for res in self.process_record(record): - Domain_Name, WARC_Target_URI, WARC_Identified_Content_Language, WARC_Date, Content_Type,Content_Length, wet_record_txt=res - if Domain_Name: - yield res - self.records_processed.add(1) + for res in self.process_record(record): + Domain_Name, WARC_Target_URI, WARC_Identified_Content_Language, WARC_Date, Content_Type,Content_Length, wet_record_txt=res + if Domain_Name: + yield (file_name,)+res + self.records_processed.add(1) @staticmethod @@ -78,6 +80,8 @@ def process_record(self, record): if len(set(WARC_Identified_Content_Languages_lst) & set(self.supported_langs)) > 0: WARC_Target_URI = record.rec_headers['WARC-Target-URI'] Domain_Name = urlparse(WARC_Target_URI).netloc + # FileName=input_file_name() + # print(f"FileName={FileName}") if Domain_Name in self.domains_pc1_dict.value: # if self.is_domain_exist(Domain_Name): # print(f"{Domain_Name} exist") @@ -124,6 +128,8 @@ def log_accumulators(self, session): def load_domain_pc1(domains_pc1_csv_path="../../data/dqr/domain_pc1.csv"): doamins_df = pd.read_csv(domains_pc1_csv_path) return dict(zip(doamins_df["domain"].tolist(), doamins_df["pc1"].tolist())) + + def run_job(self, session): self.get_logger().info(f"seed domain path={self.args.trusted_domains}") out_path=str(session.conf.get("spark.sql.warehouse.dir")).split(":")[-1]+"/"+self.args.output @@ -132,6 +138,7 @@ def run_job(self, session): shutil.rmtree(out_path) if self.args.input != '': input_data = session.sparkContext.textFile( + # input_data=session.sparkContext.wholeTextFiles( self.args.input, minPartitions=self.args.num_input_partitions ) output = input_data.mapPartitionsWithIndex(self.process_warcs) diff --git a/tgrag/content_embbeding/TFIDF.py b/tgrag/content_embbeding/TFIDF.py new file mode 100644 index 00000000..211b3e33 --- /dev/null +++ b/tgrag/content_embbeding/TFIDF.py @@ -0,0 +1,70 @@ +import argparse +import pandas as pd +import nltk +from nltk.corpus import stopwords +from nltk.stem import WordNetLemmatizer +lemmatizer = WordNetLemmatizer() +import re +import string +nltk.download('stopwords') +stop_words = stopwords.words('english') +import pickle +from sklearn.feature_extraction.text import TfidfVectorizer +def clean_text(text): + ''' + Perform stop-words removal and lemmatization + ''' + text = str(text).lower() + text = re.sub(r"[^a-zA-Z?.!,¿]+|http\S+", " ", text) + text = ''.join([char for char in text if char not in string.punctuation]) + words = [word for word in text.split() if word not in stopwords.words('english')] + words = [WordNetLemmatizer().lemmatize(word) for word in words] + return " ".join(words) +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Content Embedding") + parser.add_argument("--data_path", type=str, default="../../data/dqr/", help="dqr dataset path") + parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"], help="CrediBench month snapshot") + args = parser.parse_args() + + dqr_df = pd.read_csv(f"{args.data_path}dqr_{args.month}_domains_content.csv") + dqr_df.columns = ["html", "url"] + df_phishtank = pd.read_csv(f"{args.data_path}cc_{args.month}_2024_phishtank_domains_scraped_metadata_0.csv") + df_phishtank = df_phishtank[["html", "url"]] + df_URLhaus = pd.read_csv(f"{args.data_path}cc_{args.month}_2024_URLhaus_domains_scraped_metadata_0.csv") + df_URLhaus = df_URLhaus[["html", "url"]] + df_phishDataset_legit = pd.read_csv(f"{args.data_path}cc_{args.month}_2024_PhishDataset_legit_domains_scraped_metadata_0.csv") + df_phishDataset_legit = df_phishDataset_legit[["html", "url"]] + + df = pd.concat([dqr_df, df_phishtank, df_URLhaus, df_phishDataset_legit]) + dqr_df[dqr_df["html"].isna()] + df["final_text"] = df["html"] + ## Clean Text + df['final_text_cleaned'] = df['final_text'].apply(clean_text) + ## TFIDF + tfidf_vectorizer = TfidfVectorizer(min_df=150, ngram_range=(1, 2)) + tfidf = tfidf_vectorizer.fit_transform(df['final_text_cleaned']) + ## Write TFIDF emb + tfidf_emb_dict = {} + for i in range(0, tfidf.shape[0]): + tfidf_emb_dict[df.iloc[i]["url"]] = tfidf[i].toarray()[0] + + start_idx=0 + end_idx=len(dqr_df) + with open(f'{args.data_path}dqr_oct_TFIDF_weaksupervision_emb_{tfidf.shape[1]}.pkl', 'wb') as f: + pickle.dump(dict(list(tfidf_emb_dict.items())[start_idx:end_idx]), f) + start_idx = end_idx + end_idx+=len(df_phishtank) + with open(f'{args.data_path}phishtank_oct_TFIDF_weaksupervision_emb_{tfidf.shape[1]}.pkl', 'wb') as f: + pickle.dump(dict(list(tfidf_emb_dict.items())[start_idx:end_idx]), f) + start_idx = end_idx + end_idx += len(df_URLhaus) + with open(f'{args.data_path}URLhaus_oct_TFIDF_weaksupervision_emb_{tfidf.shape[1]}.pkl', 'wb') as f: + pickle.dump(dict(list(tfidf_emb_dict.items())[start_idx:end_idx]), f) + start_idx = end_idx + end_idx += len(df_phishDataset_legit) + with open(f'{args.data_path}phishDataset_legit_oct_TFIDF_weaksupervision_emb_{tfidf.shape[1]}.pkl', 'wb') as f: + pickle.dump(dict(list(tfidf_emb_dict.items())[start_idx:end_idx]), f) + + + + diff --git a/tgrag/content_embbeding/llm_embedding.py b/tgrag/content_embbeding/llm_embedding.py new file mode 100644 index 00000000..fe476e48 --- /dev/null +++ b/tgrag/content_embbeding/llm_embedding.py @@ -0,0 +1,36 @@ +from sentence_transformers import SentenceTransformer +import pandas as pd +from tqdm import tqdm +import pickle +import ollama +import argparse +def embd_documents(model_name,documents_path,k_col,text_col,batch_size=100, useOllama=False): + data_df=pd.read_csv(documents_path) + model = SentenceTransformer(model_name) + keys_lst = data_df[k_col].tolist() + docs_lst = data_df[text_col].astype(str).tolist() + embds = {} + for emb_batch_idx in range(0, len(keys_lst), batch_size): + # print("emb_batch_idx=", emb_batch_idx) + if useOllama: + response = ollama.embed(model="hf.co/Qwen/Qwen3-Embedding-8B-GGUF:Q4_K_M", + input=docs_lst[emb_batch_idx:emb_batch_idx + batch_size]) + batch_emb = response["embeddings"] + else: + batch_emb=model.encode(docs_lst[emb_batch_idx:emb_batch_idx+batch_size]) + for emb_idx in range(len(batch_emb)): + embds[keys_lst[emb_batch_idx + emb_idx]] = batch_emb[emb_idx] + return embds + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Content Embedding") + parser.add_argument("--documents_path", type=str, default="../../data/dqr/domain_pc1.csv", help="dqr dataset path") + parser.add_argument("--model_name", type=str, default="Qwen/Qwen3-Embedding-0.6B", help="LLM model Name", choices=["Qwen/Qwen3-Embedding-0.6B","Qwen/Qwen3-Embedding-8B","Qwen/Qwen3-Embedding-4B","google/embeddinggemma-300m"]) + parser.add_argument("--k_col", type=str, default="domain", help="document key column") + parser.add_argument("--text_col", type=str, default="text", help="document text column") + parser.add_argument("--batch_size", type=int, default=100, help="scarp batch_size") + args = parser.parse_args() + embddings=embd_documents(args.model_name,args.documents_path,args.k_col,args.text_col,args.batch_size) + # with open(f'dqr_{emb_model.split("/")[-1]}_{embds['sports.nbcsports.com'].shape[0]}.pkl', 'wb') as f: + with open(f'/shared_mnt/dqr_{args.model_name.split("/")[-1]}_{len(embddings[list(embddings.keys())[0]])}.pkl', 'wb') as f: + pickle.dump(embddings, f) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/main.py b/tgrag/experiments/mlp_experiments/main.py index fa32c819..d8605a49 100644 --- a/tgrag/experiments/mlp_experiments/main.py +++ b/tgrag/experiments/mlp_experiments/main.py @@ -1,174 +1,539 @@ import argparse import logging +from typing import Dict, cast import pickle -from typing import Dict, List, Tuple - import numpy as np +from tgrag.utils.args import parse_args +from tgrag.utils.logger import setup_logging +from tgrag.utils.path import get_root_dir +from tqdm import tqdm import pandas as pd -from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score -from sklearn.model_selection import train_test_split +import matplotlib.pyplot as plt from sklearn.neural_network import MLPRegressor -from tqdm import tqdm +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from statistics import mean +from mlp_multihead import MultiTaskMLP, train_multihead -from tgrag.utils.logger import setup_logging -from tgrag.utils.path import get_root_dir -from tgrag.utils.plot import plot_histogram, plot_loss, plot_regression_scatter - -parser = argparse.ArgumentParser(description='MLP Experiments') -parser.add_argument( - '--target', type=str, default='mbfc_bias', choices=['pc1', 'mbfc_bias'] -) -parser.add_argument('--emb_path', type=str, default='data/dqr') -parser.add_argument('--dqr_path', type=str, default='data/dqr') -parser.add_argument( - '--embed_type', type=str, default='text', choices=['text', 'domainName'] -) -parser.add_argument('--batch_size', type=int, default=5000) -parser.add_argument('--test_valid_size', type=float, default=0.4) -parser.add_argument('--emb_dim', type=int, default=1024) -parser.add_argument('--max_iter', type=int, default=200) -parser.add_argument('--lr', type=float, default=1e-3) -parser.add_argument('--epochs', type=int, default=15) -parser.add_argument('--plots_out_path', type=str, default='plots') -parser.add_argument( - '--log-file', - type=str, - default='mlp_experiment.log', - help='Name of log file at project root.', -) - - -def load_emb_dict(embed_type: str, path: str) -> Dict[str, np.ndarray]: - if embed_type == 'text': - with open(f'{path}/labeled_11k_scraped_text_emb.pkl', 'rb') as f: + +def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): + months_emb_lst = [] + if embed_type == "GNN_GAT": + for month in month_lst: + with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + months_emb_lst.append(embd_dict) + common_domains_set = set(months_emb_lst[0].keys()).intersection(months_emb_lst[1].keys()).intersection( + months_emb_lst[2].keys()) + + for key in common_domains_set: + for i in range(1, len(months_emb_lst)): + if agg == "concat": + months_emb_lst[0][key].extend(months_emb_lst[i][key]) + elif agg == "min": + months_emb_lst[0][key] = [min(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] + elif agg == "max": + months_emb_lst[0][key] = [max(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] + elif agg == "avg": + months_emb_lst[0][key] = [(a + b) / 2 for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] + + concat_dict = {k: v for k, v in months_emb_lst[0].items() if k in common_domains_set} + # print(f"concat Nmonth emb size={len(concat_dict[list(concat_dict.keys())[0]])}") + # print(f"len of keys={len(concat_dict.keys())}") + return concat_dict + + +def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): + months_emb_PhishTank_lst = [] + months_emb_URLhaus_lst = [] + months_emb_legit_lst = [] + for month in month_lst: + with open(f'{path}/PhishTank_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_PhishTank_lst.append(pickle.load(f)) + with open(f'{path}/URLHaus_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_URLhaus_lst.append(pickle.load(f)) + with open(f'{path}/IP2Location_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_legit_lst.append(pickle.load(f)) + + for ds_months in [months_emb_PhishTank_lst, months_emb_URLhaus_lst, months_emb_legit_lst]: + for key in ds_months[0].keys(): + for i in range(1, len(ds_months)): # loop on dataset months + if key in ds_months[i]: + if agg == "concat": + ds_months[0][key].extend(ds_months[i][key]) + # print(len(ds_months[0][key])) + elif agg == "min": + ds_months[0][key] = [min(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "max": + ds_months[0][key] = [max(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "avg": + ds_months[0][key] = [(a + b) / 2 for a, b in zip(ds_months[0][key], ds_months[i][key])] + # print(len(ds_months[0][key])) + return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] + + +def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1"): + if embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_768.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "IPTC_Topic_emb": + with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl', + 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "domainName": + with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + # with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "IPTC_Topic": + with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_IPTC-news-topic_scores.pkl', 'rb') as f: embd_dict = pickle.load(f) - elif embed_type == 'domainName': - with open(f'{path}/labeled_11k_domainname_emb.pkl', 'rb') as f: + elif embed_type == "IPTC_Topic_freq": + with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_topics_frequancy_norm_dict.pkl', + 'rb') as f: embd_dict = pickle.load(f) + elif embed_type == "IPTC_Topic_emb": + with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl', + 'rb') as f: + embd_dict = pickle.load(f) + # print(list(embd_dict.keys())[0],embd_dict[list(embd_dict.keys())[0]]) + elif embed_type == "3Feat": + with open(f'/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_3Feat_dict.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "3Feat2": + with open(f'/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_3Feat_dict2.pkl', 'rb') as f: + embd_dict = pickle.load(f) + + elif embed_type == "TFIDF": + # with open(f'{path}/dqr_TFIDF_emb.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_8465.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_19437.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + if month == "dec": + with open(f'{path}/dqr_dec_TFIDF_weaksupervision_emb_222755.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif month == "nov": + with open(f'{path}/dqr_nov_TFIDF_weaksupervision_emb_258729.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif month == "oct": + with open(f'{path}/dqr_oct_TFIDF_weaksupervision_emb_19085.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "PASTEL": + with open(f'{path}/dqr_pastel_dict.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "PASTEL_hasContent": + with open(f'{path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: + embd_dict = pickle.load(f) + return embd_dict -def train_valid_test_split( - target: str, labeled_11k_df: pd.DataFrame, test_valid_size: float = 0.4 -) -> Tuple[ - pd.DataFrame, List[float], pd.DataFrame, List[float], pd.DataFrame, List[float] -]: - if target == 'pc1': - quantiles = labeled_11k_df[target].quantile([0.2, 0.4, 0.6, 0.8, 1.0]) - elif target == 'mbfc_bias': +def load_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", + target="pc1", gnn_emb=None, agg=None): + embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = {}, {}, {} + if embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_768.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/cc_dec_2024_phishtank_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: + embd_dict_phishtank = pickle.load(f) + with open(f'{path}/cc_dec_2024_URLhaus_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: + embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/cc_dec_2024_PhishDataset_legit_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: + embd_dict_PhishDataset_legit = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/phishtank_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: + embd_dict_phishtank = pickle.load(f) + with open(f'{path}/URLhaus_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: + embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/PhishDataset_legit_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: + embd_dict_PhishDataset_legit = pickle.load(f) + + if gnn_emb == True: + print("len of embd_dict_phishtank before appending GNN=", + len(embd_dict_phishtank[list(embd_dict_phishtank.keys())[0]])) + if agg is None: + with open(f'{path}/PhishTank_{target}_rni_embeddings.pkl', 'rb') as f: + gnn_embd_dict_phishtank = pickle.load(f) + with open(f'{path}/URLHaus_{target}_rni_embeddings.pkl', 'rb') as f: + gnn_embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/IP2Location_{target}_rni_embeddings.pkl', 'rb') as f: + gnn_embd_dict_PhishDataset_legit = pickle.load(f) + else: + gnn_embd_dict_phishtank, gnn_embd_dict_URLhaus, gnn_embd_dict_PhishDataset_legit = load_agg_Nmonth_weaksupervision_emb_dict( + embed_type, path, model_name, month_lst=["dec", "nov", "oct"], target=target, agg=agg) + + ############# Append Emb ############# + for k in embd_dict_phishtank: + embd_dict_phishtank[k] = gnn_embd_dict_phishtank[k] + embd_dict_phishtank[k] + for k in embd_dict_URLhaus: + embd_dict_URLhaus[k] = gnn_embd_dict_URLhaus[k] + embd_dict_URLhaus[k] + for k in embd_dict_PhishDataset_legit: + embd_dict_PhishDataset_legit[k] = gnn_embd_dict_PhishDataset_legit[k] + embd_dict_PhishDataset_legit[k] + print("len of embd_dict_phishtank after appending GNN=", + len(embd_dict_phishtank[list(embd_dict_phishtank.keys())[0]])) + + + elif embed_type == "domainName": + with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "TFIDF": + # with open(f'{path}/dqr_TFIDF_emb.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_8465.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_dec_TFIDF_emb_19437.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_19437.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + emb_size = "222755" if month == "dec" else "258729" if month == "nov" else "19085" + with open(f'{path}/phishtank_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: + embd_dict_phishtank = pickle.load(f) + with open(f'{path}/URLhaus_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: + embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/phishDataset_legit_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: + embd_dict_PhishDataset_legit = pickle.load(f) + + return embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit + + +def train_valid_test_split(target, labeled_11k_df, test_valid_size=0.4): + if target == "mbfc_bias": quantiles = labeled_11k_df[target].quantile([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) + else: + quantiles = labeled_11k_df[target].quantile([0.2, 0.4, 0.6, 0.8, 1.0]) bins = [labeled_11k_df[target].min()] + quantiles.tolist() - labeled_11k_df[target + '_cat'] = pd.cut( - labeled_11k_df[target], bins=bins, labels=quantiles, include_lowest=True - ) + labeled_11k_df[target + '_cat'] = pd.cut(labeled_11k_df[target], bins=bins, labels=quantiles, include_lowest=True) X = labeled_11k_df[['domain', target]] y = labeled_11k_df[target + '_cat'] X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=test_valid_size, stratify=y, random_state=42 ) X_valid, X_test, y_valid, y_test = train_test_split( - X_test, y_test, test_size=0.5, stratify=y_test, random_state=42 - ) - return ( - X_train, - labeled_11k_df.iloc[X_train.index][target].tolist(), - X_valid, - labeled_11k_df.iloc[X_valid.index][target].tolist(), - X_test, - labeled_11k_df.iloc[X_test.index][target].tolist(), - ) + X_test, y_test, test_size=0.5, stratify=y_test, random_state=42) + return X_train, labeled_11k_df.iloc[X_train.index][target].tolist(), X_valid, labeled_11k_df.iloc[X_valid.index][ + target].tolist(), X_test, labeled_11k_df.iloc[X_test.index][target].tolist() + + +def resize_emb(text_emb, target, X_train, X_valid, X_test, gnn_emb=None, topic_emb=None, trim_to=1024): + X_train_feat = [text_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_valid_feat = [text_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_test_feat = [text_emb[d][0:trim_to] for d in X_test["domain"].tolist()] + print("emb-size=", len(X_test_feat[0])) + if gnn_emb is not None: + X_train_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_train_feat_gnn, X_train_feat)] + + X_valid_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_valid_feat_gnn, X_valid_feat)] + + X_test_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_test["domain"].tolist()] + X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_gnn, X_test_feat)] + print("emb-size=", len(X_test_feat[0])) + if topic_emb is not None: + X_train_feat_topic = [topic_emb[d] for d in X_train["domain"].tolist()] + X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in + zip(X_train_feat_topic, X_train_feat)] + + X_valid_feat_topic = [topic_emb[d] for d in X_valid["domain"].tolist()] + X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in + zip(X_valid_feat_topic, X_valid_feat)] + X_test_feat_topic = [topic_emb[d] for d in X_test["domain"].tolist()] + X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_topic, X_test_feat)] + print("emb-size=", len(X_test_feat[0])) -def resize_emb( - embds: Dict[str, np.ndarray], - X_train: pd.DataFrame, - X_valid: pd.DataFrame, - X_test: pd.DataFrame, - trim_to: int = 1024, -) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]: - X_train_feat = [embds[d][0:trim_to] for d in X_train['domain'].tolist()] - X_valid_feat = [embds[d][0:trim_to] for d in X_valid['domain'].tolist()] - X_test_feat = [embds[d][0:trim_to] for d in X_test['domain'].tolist()] return X_train_feat, X_valid_feat, X_test_feat -def train_mlp( - mlp_reg: MLPRegressor, - X_train_feat: List[np.ndarray], - Y_train: List[float], - X_valid_feat: List[np.ndarray], - Y_valid: List[float], - X_test_feat: List[np.ndarray], - Y_test: List[float], - epochs: int = 15, -) -> Tuple[MLPRegressor, List[float], List[float], List[float], List[float]]: +def train_mlp(mlp_reg, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, epochs=15): batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] for _ in tqdm(range(epochs)): - for b in range(batch_size, len(Y_train), batch_size): - X_batch, y_batch = ( - X_train_feat[b - batch_size : b], - Y_train[b - batch_size : b], - ) + for b in range(0, len(Y_train), batch_size): + X_batch, y_batch = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] batch_mean = sum(y_batch) / len(y_batch) mlp_reg.partial_fit(X_batch, y_batch) train_loss.append(mlp_reg.loss_) - valid_loss.append( - mean_squared_error(Y_valid, mlp_reg.predict(X_valid_feat)) - ) + valid_loss.append(mean_squared_error(Y_valid, mlp_reg.predict(X_valid_feat))) test_loss.append(mean_squared_error(Y_test, mlp_reg.predict(X_test_feat))) - mean_loss.append( - mean_squared_error(y_batch, [batch_mean for elem in y_batch]) - ) + mean_loss.append(mean_squared_error(y_batch, [batch_mean for elem in y_batch])) return mlp_reg, train_loss, valid_loss, test_loss, mean_loss -def eval(pred: np.ndarray, true: List[float]) -> Tuple[float, float, float]: +def plot_loss(train_loss, valid_loss, test_loss, mean_loss, out_path="../../visualizations", target="pc1", + embed_type="text", emb_model="", use_gnn_emb=False, use_topic_emb=False, run=1, month="dec"): + plt.figure(figsize=(5, 4)) + plt.rc('font', size=16) + plt.plot(range(len(train_loss)), train_loss, label="train loss") + plt.plot(range(len(train_loss)), valid_loss, label="validation loss") + plt.plot(range(len(train_loss)), test_loss, label="test loss") + plt.plot(range(len(train_loss)), mean_loss, label="mean loss") + plt.xticks(range(0, len(train_loss) + 1, 1 if len(train_loss) <= 10 else len(train_loss) // 10)) + plt.xlabel("Epoch") + plt.ylabel("MSE") + plt.grid(True) + plt.legend(fontsize=14) + plt.savefig( + f"{out_path}/dqr_{month}_{target}_{embed_type}_{emb_model}_{'+GNN-RNI' if use_gnn_emb else ''}_run{str(run)}_loss_128-200-15-60-20-00.pdf", + bbox_inches='tight', pad_inches=0.1) + plt.show() + + +def plot_histogram(true, pred, out_path="../../visualizations", target="pc1", embed_type="text", emb_model="", + use_gnn_emb=False, use_topic_emb=False, run=1, month="dec"): + plt.figure(figsize=(5, 4)) + plt.hist(pred, bins=50, range=(0, 1), edgecolor='black', color='lightblue', label="Pred") + plt.hist(true, bins=50, range=(0, 1), edgecolor='black', color='orange', alpha=0.6, label="True") + y_max = max(np.histogram(true, bins=50, range=(0, 1))[0].max(), + np.histogram(pred, bins=50, range=(0, 1))[0].max()) + plt.rc('font', size=15) + plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') + plt.yticks(np.arange(0, y_max + 50, 100), rotation=0, ha='right') + plt.xlabel(target.upper().replace("_", "-")) + plt.ylabel('Frequancy') + plt.legend() + plt.savefig( + f"{out_path}/dqr_{month}_{target}_{embed_type}_{emb_model}_{'GNN-RNI' if use_gnn_emb else ''}_{'topic-emb' if use_topic_emb else ''}_{'topic-emb' if use_topic_emb else ''}_run{str(run)}_testset_true_vs_pred_frequancy.pdf", + bbox_inches='tight', pad_inches=0.1) + plt.show() + + +def plot_regression_scatter(true, pred, out_path="../../visualizations", target="pc1", embed_type="text", emb_model="", + use_gnn_emb=False, use_topic_emb=False, run=1, month="dec"): + plt.figure(figsize=(5, 4)) + plt.rc('font', size=16) + plt.scatter(true, pred, alpha=0.7) + plt.plot([0, 1], [0, 1], color='red', linestyle='-', label='regression line') + plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') + plt.yticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') + plt.xlabel(target.upper().replace("_", "-")) + plt.ylabel(f"Predicted {target.upper().replace('_', '-')}") + plt.legend() + plt.grid(True) + plt.savefig( + f"{out_path}/dqr_{month}_{target}_{embed_type}_{emb_model}_{'GNN-RNI' if use_gnn_emb else ''}_{'topic-emb' if use_topic_emb else ''}_run{str(run)}_testset_true_vs_pred_scatter.pdf", + bbox_inches='tight', pad_inches=0.1) + plt.show() + + +def eval(pred, true): + max(abs(x - y) for x, y in zip(true, pred)) + res_df = pd.DataFrame(zip(true, pred), columns=['true', 'pred']) + res_df["diff"] = res_df.apply(lambda row: abs(row['true'] - row['pred']), axis=1) + max_idx = res_df.idxmax()["diff"] + min_idx = res_df.idxmin()["diff"] + max_diff_row = res_df.iloc[max_idx] + min_diff_row = res_df.iloc[min_idx] + max_diff_dict = max_diff_row.to_dict() + max_diff_dict["test_idx"] = max_idx + min_diff_dict = min_diff_row.to_dict() + min_diff_dict["test_idx"] = min_idx + mse = mean_squared_error(true, pred) + # print(f"mse={mse}") r2 = r2_score(true, pred) + # print(f"r2={r2}") mae = mean_absolute_error(true, pred) - return mse, mae, r2 + true_mean = mean(true) + mean_mae = mean_absolute_error(true, [true_mean for elem in true]) + # print(f"MAE={mae}") + return mse, mae, r2, mean_mae, min_diff_dict, max_diff_dict def main() -> None: - root = get_root_dir() + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP Experiments") + parser.add_argument("--target", type=str, default="pc1", choices=["pc1", "mbfc", "mbfc_bias"], help="the credability target") + parser.add_argument("--emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--dqr_path", type=str, default=str(root + "/data/dqr"),help="dqr dataset path") + parser.add_argument("--embed_type", type=str, default="text", + choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") + parser.add_argument("--emb_model", type=str, default="embeddingTE3L", + choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", + "IPTC_Topic_emb"],help="LLM embedding model") + parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") + parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") + parser.add_argument("--emb_dim", type=int, default=4096,help="embedding size") + parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") + # parser.add_argument("--lr", type=float, default=3e-5) + parser.add_argument("--lr", type=float, default=5e-3,help="learning rate") + parser.add_argument("--epochs", type=int,default=14,help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L + parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") + parser.add_argument("--runs", type=int, default=1,help="# training runs") + parser.add_argument("--use_gnn_emb", type=bool, default=False,help="append GNN embedding") + parser.add_argument("--agg_month_emb", type=bool, default=False, help="aggregate montly GNN embeddings") + parser.add_argument("--use_topic_emb", type=bool, default=False,help="use topic modeling features") + parser.add_argument("--filter_by_GNN_nodes", type=bool, default=True,help="filter by domains has GNN embeddings") + parser.add_argument("--filter_by_PASTEL_domains", type=bool, default=False,help="filter by domains has PASTEL embeddings") + parser.add_argument("--MultiHead", type=bool, default=True, help="Use MultiHead Model") + parser.add_argument("--num_classes", type=int, default=3,help="# classifcation classes for Multihead model") + parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") + parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") args = parser.parse_args() - setup_logging(args.log_file) - emb_dict = load_emb_dict(args.embed_type, root / args.emb_path) - labeled_11k_df = pd.read_csv(root / 'dqr' / 'domain_ratings.csv') - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split( - args.target, labeled_11k_df, args.test_valid_size - ) - X_train_feat, X_valid_feat, X_test_feat = resize_emb( - emb_dict, X_train, X_valid, X_test, args.emb_dim - ) - mlp_reg = MLPRegressor( - hidden_layer_sizes=(128, 64), - activation='relu', - solver='adam', - max_iter=args.max_iter, - random_state=42, - verbose=False, - learning_rate_init=args.lr, - ) - mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_mlp( - mlp_reg, - X_train_feat, - y_train, - X_valid_feat, - y_valid, - X_test_feat, - y_test, - epochs=args.epochs, - ) - true = y_test - pred = mlp_reg.predict(X_test_feat) + print("args=", args) + ############## Load training data and split ############### + text_emb_dict = load_emb_dict(args.embed_type, args.emb_path, args.emb_model, args.month) + labeled_11k_df = pd.read_csv(f"{args.dqr_path}/domain_ratings.csv") + ############### filter by the 8K GNN Nodes ################### + if args.filter_by_GNN_nodes: + targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") + targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(targets_nodes_df["domain_rev"])] + labeled_11k_df = labeled_11k_df.reset_index(drop=True) + if args.filter_by_PASTEL_domains: + pastel_emb_dict = {} + with open(f'{args.dqr_path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: + pastel_emb_dict = pickle.load(f) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(pastel_emb_dict.keys())] + labeled_11k_df = labeled_11k_df.reset_index(drop=True) + ################# Train ##################### + results = [] + for i in range(args.runs): + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, labeled_11k_df, + args.test_valid_size) + if args.embed_type == "TFIDF": + print(f"TFIDF dim={text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]}") + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, X_test, + gnn_emb=None, trim_to= + text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]) + else: + if args.use_gnn_emb or args.use_topic_emb: + if args.use_gnn_emb: + if args.agg_month_emb: + gnn_emb_dict = load_agg_Nmonth_emb_dict("GNN_GAT", args.emb_path, agg="avg") + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(gnn_emb_dict.keys())] + labeled_11k_df = labeled_11k_df.reset_index(drop=True) + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, + labeled_11k_df, + args.test_valid_size) + else: + gnn_emb_dict = load_emb_dict("GNN_GAT", args.emb_path) + else: + gnn_emb_dict = None + if args.use_topic_emb: + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_freq", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_emb", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("3Feat", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("3Feat2", args.emb_path) + # gnn_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) + topic_iptc_emb_dict = load_emb_dict("PASTEL_hasContent", args.emb_path) + else: + topic_iptc_emb_dict = None + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, + X_test, gnn_emb=gnn_emb_dict, + topic_emb=topic_iptc_emb_dict, + trim_to=args.emb_dim) + else: + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, + X_test, gnn_emb=None, topic_emb=None, + trim_to=args.emb_dim) + + print(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") + if args.MultiHead == False: + # mlp_reg = MLPRegressor(hidden_layer_sizes=(128, 64), activation='relu', solver='adam', max_iter=args.max_iter,random_state=42, verbose=False, learning_rate_init=args.lr,alpha=0.001) + mlp_reg = MLPRegressor(hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16), + activation='relu', solver='adam', max_iter=args.max_iter, random_state=42, + verbose=False, learning_rate_init=args.lr) + mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_mlp(mlp_reg, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + else: + mlp_reg = MultiTaskMLP(input_dim=len(X_train_feat[0]), num_classes=args.num_classes, + hidden_dims=[int(len(X_train_feat[0]) * 0.9), 64, 16]) + mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_multihead(mlp_reg, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, lr=args.lr, + epochs=args.epochs, w_reg=0.5, + w_cls=0.5, + num_classes=args.num_classes) + ################## Weak Supervision ########### + if args.generate_weaksupervision_scores == True: + if args.agg_month_emb: + embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = load_weaksupervision_emb_dict( + args.embed_type, args.emb_path, args.emb_model, month="dec", target=args.target, + gnn_emb=args.use_gnn_emb, agg="avg") + else: + embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = load_weaksupervision_emb_dict( + args.embed_type, args.emb_path, args.emb_model, month="dec", target=args.target, + gnn_emb=args.use_gnn_emb, agg=None) + + phishtank_features = [v for k, v in embd_dict_phishtank.items()] + phishtank_pred = mlp_reg.predict(phishtank_features) + pd.DataFrame(zip(embd_dict_phishtank.keys(), phishtank_pred), + columns=["domain", f"pred_{args.target}"]).to_csv( + f"{args.plots_out_path}/Phishtank_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}_{'_+GNN-RNI' if args.use_gnn_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", + index=None) + # print("phishtank_pred=",phishtank_pred) + + URLhaus_features = [v for k, v in embd_dict_URLhaus.items()] + URLhaus_pred = mlp_reg.predict(URLhaus_features) + pd.DataFrame(zip(embd_dict_URLhaus.keys(), URLhaus_pred), columns=["domain", f"pred_{args.target}"]).to_csv( + f"{args.plots_out_path}/URLhaus_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}_{'_+GNN-RNI' if args.use_gnn_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", + index=None) + + PhishDataset_legit_features = [v for k, v in embd_dict_PhishDataset_legit.items()] + PhishDataset_legit_pred = mlp_reg.predict(PhishDataset_legit_features) + pd.DataFrame(zip(embd_dict_PhishDataset_legit.keys(), PhishDataset_legit_pred), + columns=["domain", f"pred_{args.target}"]).to_csv( + f"{args.plots_out_path}/PhishDataset_legit_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}_{'_+GNN-RNI' if args.use_gnn_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", + index=None) + ################## Plot and Eval ############### + true = y_test + pred = mlp_reg.predict(X_test_feat) + plot_loss(train_loss, valid_loss, test_loss, mean_loss, args.plots_out_path, target=args.target, + emb_model=args.emb_model, use_gnn_emb=args.use_gnn_emb, use_topic_emb=args.use_topic_emb, + embed_type=args.embed_type, run=i, month=args.month) + plot_histogram(true, pred, args.plots_out_path, target=args.target, emb_model=args.emb_model, + use_gnn_emb=args.use_gnn_emb, use_topic_emb=args.use_topic_emb, embed_type=args.embed_type, + run=i, month=args.month) + plot_regression_scatter(true, pred, args.plots_out_path, target=args.target, emb_model=args.emb_model, + use_gnn_emb=args.use_gnn_emb, use_topic_emb=args.use_topic_emb, + embed_type=args.embed_type, run=i, month=args.month) + MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(pred, true) + min_error_dict["domain"] = X_test.iloc[min_error_dict["test_idx"]]["domain"] + max_error_dict["domain"] = X_test.iloc[max_error_dict["test_idx"]]["domain"] + results.append([MSE, MAE, R2, Mean_MAE, str(min_error_dict), str(max_error_dict), str(args)]) + print( + f"Run{i}:MSE={MSE}\tR2={R2}\tMAE={MAE}\tMean_MAE={Mean_MAE}\tmin_error_dict={min_error_dict}\tmax_error_dict={max_error_dict}") + ############ save test results ############ + X_test.to_csv( + f"{args.plots_out_path}/dqr_testset_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv") + pd.DataFrame(zip(true, pred), columns=["true", "pred"]).to_csv( + f"{args.plots_out_path}/dqr_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", + index=None) - plot_loss(train_loss, valid_loss, test_loss, mean_loss, args.plots_out_path) - plot_histogram(true, pred, args.plots_out_path) - plot_regression_scatter(true, pred, args.plots_out_path) - MSE, MAE, R2 = eval(pred, true) - logging.info(f'MSE={MSE}\tR2={R2}\tMAE={MAE}') + results_df = pd.DataFrame(results, columns=['MSE', 'MAE', 'R2', 'Mean_MAE', 'Min_AE', 'Max_AE', 'args']) + results_df.to_csv( + f"{args.plots_out_path}/dqr_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_+IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}_result.csv", ) + for col in ['MSE', 'MAE', 'R2', 'Mean_MAE']: + print(f"{col}: Mean={results_df[col].mean()}\tstd={results_df[col].std()}") if __name__ == '__main__': - main() + main() \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/mlp_multihead.py b/tgrag/experiments/mlp_experiments/mlp_multihead.py new file mode 100644 index 00000000..9fa51a9f --- /dev/null +++ b/tgrag/experiments/mlp_experiments/mlp_multihead.py @@ -0,0 +1,109 @@ +# ---------------------------- +# Required imports +# ---------------------------- +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, TensorDataset +import numpy as np +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from tqdm import tqdm +import random + +# ---------------------------- +seed = 42 +torch.manual_seed(seed) +random.seed(seed) + + +class MultiTaskMLP(nn.Module): + def __init__(self, input_dim, num_classes, hidden_dims=[128, 64]): + super(MultiTaskMLP, self).__init__() + + # Shared feature extractor + layers = [] + in_dim = input_dim + for h in hidden_dims: + layers.append(nn.Linear(in_dim, h)) + layers.append(nn.ReLU()) + # layers.append(nn.Dropout(0.3)) + # layers.append(nn.BatchNorm1d(h)) + in_dim = h + self.trunk = nn.Sequential(*layers) + + # Task-specific heads + self.reg_head = nn.Linear(in_dim, 1) # Regression + self.cls_head = nn.Linear(in_dim, num_classes) # Multi-class classification + + def forward(self, x): + z = self.trunk(x) + reg_out = self.reg_head(z) # (batch, 1) + cls_logits = self.cls_head(z) # (batch, num_classes) + cls_pred = torch.argmax(cls_logits, dim=1) + return reg_out, cls_logits, cls_pred + + def predict(self, x): + reg_out, cls_logits, cls_pred = self.forward(torch.tensor(x)) + return reg_out.detach().squeeze(1).numpy() + + +def train_multihead(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15, + num_classes=3, w_reg=0.8, w_cls=0.2): + batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion_reg = nn.MSELoss() + criterion_cls = nn.CrossEntropyLoss() + w_reg = w_reg + w_cls = w_cls + for epoch in tqdm(range(epochs)): + for b in range(0, len(Y_train), batch_size): + X_batch, y_reg = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] + y_cls = torch.tensor([int(((elem * 10) % 10) / (num_classes + 1)) for elem in y_reg], dtype=torch.long) + batch_mean = sum(y_reg) / len(y_reg) + reg_out, cls_logits, cls_pred = model(torch.tensor(X_batch)) + # Loss calculations + loss_reg = criterion_reg(reg_out.squeeze(1), torch.tensor(y_reg)) + loss_cls = criterion_cls(cls_logits, y_cls) + # Weighted multi-task loss + train_loss_val = w_reg * loss_reg + w_cls * loss_cls + train_loss.append(train_loss_val.detach().numpy()) + val_reg_out, val_cls_logits, val_cls_pred = model(torch.tensor(X_valid_feat)) + valid_loss.append(criterion_reg(val_reg_out.detach().squeeze(1), torch.tensor(Y_valid)) * w_reg) + test_reg_out, test_cls_logits, test_cls_pred = model(torch.tensor(X_test_feat)) + test_loss.append(criterion_reg(test_reg_out.detach().squeeze(1), torch.tensor(Y_test)) * w_reg) + mean_loss.append(mean_squared_error(y_reg, [batch_mean for elem in y_reg])) + if epochs>(epoch+1): + train_loss_val.backward() + optimizer.step() + return model, train_loss, valid_loss, test_loss, mean_loss + + +def train_multihead_v0(model, epochs=100): + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + criterion_reg = nn.MSELoss() + criterion_cls = nn.CrossEntropyLoss() + # Task weights + w_reg = 0.4 + w_cls = 0.6 + # Dummy batch + x = torch.randn(32, 100) + y_reg = torch.tensor(np.arange(0, 32), dtype=torch.float64) + y_cls = torch.tensor([int(elem) % 2 for elem in y_reg], dtype=torch.long) + y_reg = y_reg.reshape(32, 1).float() + for i in range(epochs): + # Forward pass + reg_out, cls_logits, cls_pred = model(x) + # Loss calculations + loss_reg = criterion_reg(reg_out, y_reg) + loss_cls = criterion_cls(cls_logits, y_cls) + print(f"loss_reg={loss_reg}\tcls_logits={cls_logits}") + # Weighted multi-task loss + loss = w_reg * loss_reg + w_cls * loss_cls + print(loss) + loss.backward() + optimizer.step() + +if __name__ == '__main__': + model = MultiTaskMLP(input_dim=100, num_classes=2, hidden_dims=[1024, 256, 32]) + train_multihead_v0(model, 100) + diff --git a/uv.lock b/uv.lock index d67230e5..b0bfd2be 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 2 requires-python = ">=3.9" resolution-markers = [ "python_full_version < '3.10'", @@ -11,9 +12,9 @@ resolution-markers = [ name = "aiohappyeyeballs" version = "2.6.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 } +sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = 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size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, ] From f11f391977ef4220d848806679a8b413c387e36f Mon Sep 17 00:00:00 2001 From: hussein Date: Mon, 29 Dec 2025 18:30:44 -0500 Subject: [PATCH 2/8] Generate Content Embeddings Filter CC-Crawl Index --- bash_scripts/CC-Crawls/Feb2025.txt | 1 + bash_scripts/end-to-end.sh | 20 +- bash_scripts/get_data.sh | 38 +- bash_scripts/run_filter_warc-index.sh | 67 ++++ bash_scripts/run_in_batches_warc-index.sh | 11 + mila-commands.txt | 12 + pyproject.toml | 4 + tgrag/cc-scripts/filter_cc_index.py | 306 ++++++++++++++++ tgrag/cc-scripts/sparkcc.py | 5 + .../cc-scripts/wet_extract_domain_content.py | 16 +- .../generate_content_embedding.py | 58 +++ .../scrape_home_pages.py | 0 uv.lock | 344 +++++++++++++++++- 13 files changed, 860 insertions(+), 22 deletions(-) create mode 100644 bash_scripts/CC-Crawls/Feb2025.txt create mode 100755 bash_scripts/run_filter_warc-index.sh create mode 100755 bash_scripts/run_in_batches_warc-index.sh create mode 100644 mila-commands.txt create mode 100644 tgrag/cc-scripts/filter_cc_index.py create mode 100644 tgrag/content_embbeding/generate_content_embedding.py rename tgrag/{webScrape => webScraper}/scrape_home_pages.py (100%) diff --git a/bash_scripts/CC-Crawls/Feb2025.txt b/bash_scripts/CC-Crawls/Feb2025.txt new file mode 100644 index 00000000..cbe0cd41 --- /dev/null +++ b/bash_scripts/CC-Crawls/Feb2025.txt @@ -0,0 +1 @@ +CC-MAIN-2025-08 \ No newline at end of file diff --git a/bash_scripts/end-to-end.sh b/bash_scripts/end-to-end.sh index abaf6856..a1d3b6d9 100755 --- a/bash_scripts/end-to-end.sh +++ b/bash_scripts/end-to-end.sh @@ -68,7 +68,9 @@ for data_type in "${cc_file_types[@]}" ; do echo "Processing $CRAWL..." echo "Removing previous $CRAWL spark-warehouse" rm -rf "$PROJECT_ROOT/bash_scripts/spark-warehouse/$CRAWL" + rm -rf "$PROJECT_ROOT/bash_scripts/spark-warehouse/warc_index_table_$CRAWL" echo $CRAWL + CRAWL_LowerCase=${CRAWL,,} echo "################################### run get data @ $(date '+%Y-%m-%d %H:%M:%S') ###################################" echo "$SCRIPT_DIR/get_data.sh" "$CRAWL" $start_idx $end_idx "[$data_type]" "$SCRIPT_DIR/get_data.sh" "$CRAWL" $start_idx $end_idx "[$data_type]" @@ -84,13 +86,23 @@ for data_type in "${cc_file_types[@]}" ; do elif [ "$data_type" = "wet" ]; then echo "##################### run_wet_content_extraction @ $(date '+%Y-%m-%d %H:%M:%S') #####################" if [ -z "$SCRATCH" ]; then - rm -rf "$PROJECT_ROOT/bash_scripts/spark-warehouse/wet_${spark_table_name}_${CRAWL//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running + rm -rf "$PROJECT_ROOT/bash_scripts/spark-warehouse/wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running else - rm -rf "$SCRATCH/spark-warehouse/wet_${spark_table_name}_${CRAWL//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running + rm -rf "$SCRATCH/spark-warehouse/wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running fi - echo "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL//-/}_${start_idx}_${end_idx}" "$seed_list" - "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL//-/}_${start_idx}_${end_idx}" "$seed_list" + echo "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" + "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" echo "wet_extract_content_table constructed for $CRAWL batch_${start_idx}_${end_idx}" + elif [ "$data_type" = "cc-index-table" ]; then + echo "##################### run_filter_index @ $(date '+%Y-%m-%d %H:%M:%S') #####################" + if [ -z "$SCRATCH" ]; then + rm -rf "$PROJECT_ROOT/bash_scripts/spark-warehouse/cc-index-table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running + else + rm -rf "$SCRATCH/spark-warehouse/cc-index-table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running + fi + echo "$SCRIPT_DIR/run_filter_warc-index.sh" "$CRAWL" "warc_index_table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" + "$SCRIPT_DIR/run_filter_warc-index.sh" "$CRAWL" "warc_index_table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" + echo "filter_warc-index table constructed for $CRAWL batch_${start_idx}_${end_idx}" fi echo "********************** End Of $data_type Task @ $(date '+%Y-%m-%d %H:%M:%S') **********************" done diff --git a/bash_scripts/get_data.sh b/bash_scripts/get_data.sh index 8e8bbd33..209a42dd 100755 --- a/bash_scripts/get_data.sh +++ b/bash_scripts/get_data.sh @@ -11,7 +11,9 @@ fetch_with_retries() { --retry-on-http-error=429,500,502,503,504 \ --timeout=60 --read-timeout=60 \ -O "$tmp" "$url" && status=0 || status=$? - if [[ $status -eq 0 ]] && gzip -t "$tmp" 2>/dev/null; then +# echo "status=$status tmp=$tmp" + if [[ $status -eq 0 ]] && ([[ $url = *.parquet ]] || ([[ $url = *.gz ]] && gzip -t "$tmp" 2>/dev/null)); then + echo "url=$url" mv -f "$tmp" "$out" return 0 fi @@ -103,10 +105,16 @@ for data_type in "${cc_file_types[@]}" ; do input="$INPUT_DIR/all_${data_type}_${CRAWL}.txt" echo "All ${data_type} files of ${CRAWL}: $input" - listing_content=$(gzip -dc "$listing") all_listing_content_path="$INPUT_DIR/test_all_${data_type}.txt" - echo "file:$listing_content" >>"$all_listing_content_path" + if [[ "$data_type" = "cc-index-table" ]]; then + listing_content=$(gzip -dc "$listing" | grep -F "/subset=warc/") + echo "cc-index-table listing_content=$listing_content" + else + listing_content=$(gzip -dc "$listing") + fi + + echo "file:$listing_content" >>"$all_listing_content_path" listing_FilesCount=$(wc -l <<< "$listing_content") echo "listing_FilesCount=$listing_FilesCount" if [ "$listing_FilesCount" -lt "$end_idx" ] ; then @@ -137,15 +145,29 @@ for data_type in "${cc_file_types[@]}" ; do : > "$fail_log" while IFS= read -r wat_file; do - first=$(echo "$wat_file" | awk -F '/'$data_type'/' '{print $1}') + echo "wat_file=$wat_file" + if [[ "$data_type" = "cc-index-table" ]]; then + first=$(echo "$wat_file" | awk -F '/subset=warc/' '{print $1}') + else + first=$(echo "$wat_file" | awk -F '/'$data_type'/' '{print $1}') + fi + echo "first=$first" file_path="$DATA_DIR/$wat_file" - target_dir="$DATA_DIR/$first/$data_type/" + echo "file_path=$file_path" + if [[ "$data_type" = "cc-index-table" ]]; then + target_dir="$DATA_DIR/$first/subset=warc/" + else + target_dir="$DATA_DIR/$first/$data_type/" + fi + echo "target_dir=$target_dir" target_file="${target_dir}$(basename "$wat_file")" + echo "target_file=$target_file" if [ -f "$file_path" ] || [ -f "$target_file" ]; then # verify gzip; re-download if corrupt test -f "$file_path" && cand="$file_path" || cand="$target_file" - if gzip -t "$cand" 2>/dev/null; then + echo "cand=$cand" + if [[ $cand = *.parquet ]] || ([[ $cand = *.gz ]] && gzip -t "$cand" 2>/dev/null); then echo "File '$cand' exists." downloaded=$((downloaded+1)) continue @@ -168,7 +190,5 @@ for data_type in "${cc_file_types[@]}" ; do fi done <<< "$wat_files" - echo "[SUMMARY] $CRAWL type=$data_type downloaded=$downloaded skipped=$skipped fail_log=$fail_log" - -done +done \ No newline at end of file diff --git a/bash_scripts/run_filter_warc-index.sh b/bash_scripts/run_filter_warc-index.sh new file mode 100755 index 00000000..e4a459f8 --- /dev/null +++ b/bash_scripts/run_filter_warc-index.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +# Fail on first error +set -e + +# Check if CRAWL argument is provided +if [ -z "$1" ]; then + echo "Usage: $0 " + echo "Example: $0 CC-MAIN-2017-13" + exit 1 +fi +CRAWL="$1" + +if [ -z "$2" ]; then + outputTableName="filter_warcindex_table" +else + outputTableName="$2" +fi + +if [ -z "$3" ]; then + seed_list='../data/dqr/domain_pc1.csv' +else + seed_list=$3 +fi +echo "seed_list=$seed_list" + +# Get the root of the project (one level above this script's directory) +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" +VENV_PATH="$PROJECT_ROOT/.venv" + +# Use SCRATCH if defined, else fallback to project-local data dir +# For cluster usage +if [ -z "$SCRATCH" ]; then + DATA_DIR="$PROJECT_ROOT/data" +else + DATA_DIR="$SCRATCH" +fi + +INPUT_DIR="$DATA_DIR/crawl-data/$CRAWL/input" + +# Activate the virtual environment +source "$VENV_PATH/bin/activate" + +# Set PySpark to use the virtualenv's Python +export PYSPARK_PYTHON="$VENV_PATH/bin/python" +export PYSPARK_DRIVER_PYTHON="$VENV_PATH/bin/python" + + +# Run the Spark job +# Local testing: use "$INPUT_DIR/test_wat.txt" +# Cluster / full usage: ""$INPUT_DIR/all_wat_$CRAWL.txt" +# --trusted_domains "../data/dqr/domain_pc1.csv"\ +# --trusted_domains "../common_urls_set_84k.csv" \ +# "$PROJECT_ROOT/tgrag/cc-scripts/wet_extract_domain_content_11k.py" +echo "INPUT_DIR=$INPUT_DIR" +"$VENV_PATH/bin/spark-submit" \ + --driver-memory 10g \ + --executor-memory 5g \ + --py-files "$PROJECT_ROOT/tgrag/cc-scripts/sparkcc.py" \ + "$PROJECT_ROOT/tgrag/cc-scripts/filter_cc_index.py" \ + "$INPUT_DIR/test_cc-index-table.txt" \ + "$outputTableName" \ + --trusted_domains "$seed_list" \ + --output_format "parquet" \ + --output_compression "snappy" \ + --log_level "WARN" \ No newline at end of file diff --git a/bash_scripts/run_in_batches_warc-index.sh b/bash_scripts/run_in_batches_warc-index.sh new file mode 100755 index 00000000..d22cf8d6 --- /dev/null +++ b/bash_scripts/run_in_batches_warc-index.sh @@ -0,0 +1,11 @@ +#!/bin/bash +set -e +batch_size=2 +data_type="cc-index-table" +for ((i=0; i<300; i+=$batch_size)); do + echo "#########################################################################################" + echo "/end-to-end.sh CC-Crawls/Feb2025.txt $i $((i+batch_size-1)) [$data_type]" + ./end-to-end.sh CC-Crawls/Feb2025.txt $i $((i+batch_size-1)) [$data_type] + rm -r ../data/cc-index/table/cc-main/warc/crawl=CC-MAIN-2025-08/subset=warc +done + diff --git a/mila-commands.txt b/mila-commands.txt new file mode 100644 index 00000000..7496380b --- /dev/null +++ b/mila-commands.txt @@ -0,0 +1,12 @@ +- UV env activate + source .venv/bin/activate + +- upload content-embedding-to_HF +huggingface-cli upload ComplexDataLab/CrediBench +./content_emb/dec2024_wetcontent_embeddinggemma-300m/Credibench_dec2024_wetcontent_embeddinggemma-300m/ +content_embedding/dec2024/embeddinggema-300m/ + --repo-type dataset --commit-message "Add dec2024 content embedding gemma-300m" + + - squeue -u abdallah + - sbatch job_content_emb_nov2024.sh 8 8 30 + diff --git a/pyproject.toml b/pyproject.toml index 7c2550f5..07fe128d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,10 @@ dependencies = [ "jsonpath-ng>=1.7.0", "ollama>=0.5.0", "nltk", + "lightgbm>=3.3.5", + "pandas", + "pyarrow", + "huggingface-hub", ] [tool.flit.module] diff --git a/tgrag/cc-scripts/filter_cc_index.py b/tgrag/cc-scripts/filter_cc_index.py new file mode 100644 index 00000000..fc935575 --- /dev/null +++ b/tgrag/cc-scripts/filter_cc_index.py @@ -0,0 +1,306 @@ +import pandas as pd +from pyspark.sql.types import IntegerType, StringType, StructField, StructType,ArrayType +from sparkcc import CCSparkJob +# os.environ["PYSPARK_SUBMIT_ARGS"] = "--driver-memory MEM 4g" +from urllib.parse import urljoin, urlparse +from jsonpath_ng import jsonpath, parse as jsonpath_parse +import os +import shutil +import requests +from pyspark.sql import SparkSession +from pyspark.sql import Window +from pyspark.sql.functions import row_number +from pyspark.sql import functions as F +import boto3 +import botocore +from io import BytesIO +import pyarrow.parquet as pq +from tempfile import SpooledTemporaryFile, TemporaryFile +from pyspark.sql.functions import input_file_name +class Filter_CC_Index_Job(CCSparkJob): + """Filter_CC_Index_Job . """ + num_input_partitions = 64 + num_output_partitions = 4 + name = 'Filter_CC_Index_Job' + cc_index_cols=['url_surtkey','url','url_host_name','url_host_tld','url_host_2nd_last_part','url_host_3rd_last_part','url_host_4th_last_part','url_host_5th_last_part','url_host_registry_suffix','url_host_registered_domain','url_host_private_suffix','url_host_private_domain','url_host_name_reversed','url_protocol','url_port','url_path','url_query','fetch_time','fetch_status','fetch_redirect','content_digest','content_mime_type','content_mime_detected','content_charset','content_languages','content_truncated','warc_filename','warc_record_offset','warc_record_length','warc_segment'] + index_toread_cols=['url','url_host_name', 'content_languages', 'warc_filename','warc_record_offset','warc_record_length','warc_segment'] + output_schema = StructType( + [StructField('FileName', StringType(), True), + StructField('url', StringType(), True), + StructField('url_host_name', StringType(), True), + StructField('content_languages', StringType(), True), + StructField('warc_filename', StringType(), True), + StructField('warc_record_offset', IntegerType(), True), + StructField('warc_record_length', IntegerType(), True), + StructField('warc_segment', StringType(), True), + ] + ) + parquet_records = None + parquet_credibench_records = None + records_failed = None + domains_pc1_dict=None + supported_langs = ["eng", "fra"] # "language codes: ISO-639-3 " + filter_by_languages=False + + + def add_arguments(self, parser): + parser.add_argument( + '--intermediate_output', + type=str, + default=None, + help='Intermediate output to recover job from', + ) + + def fetch_parquet(self, uri, base_uri=None, offset=-1, length=-1): + """Fetch WARC/WAT/WET files (or a record if offset and length are given)""" + (scheme, netloc, path) = (None, None, None) + uri_match = self.data_url_pattern.match(uri) + if not uri_match and base_uri: + # relative input URI (path) and base URI defined + uri = base_uri + uri + uri_match = self.data_url_pattern.match(uri) + + if uri_match: + (scheme, netloc, path) = uri_match.groups() + else: + # keep local file paths as is + path = uri + + stream = None + + if scheme == 's3': + bucketname = netloc + if not bucketname: + self.get_logger().error('Invalid S3 URI: ' + uri) + return + if not path: + self.get_logger().error('Empty S3 path: ' + uri) + return + elif path[0] == '/': + # must strip leading / in S3 path + path = path[1:] + if offset > -1 and length > 0: + rangereq = 'bytes={}-{}'.format(offset, (offset + length - 1)) + # Note: avoid logging too many small fetches + # self.get_logger().debug('Fetching {} ({})'.format(uri, rangereq)) + try: + response = self.get_s3_client().get_object( + Bucket=bucketname, Key=path, Range=rangereq + ) + stream = BytesIO(response['Body'].read()) + except botocore.client.ClientError as exception: + self.get_logger().error( + 'Failed to download: s3://{}/{} (offset: {}, length: {}) - {}'.format( + bucketname, path, offset, length, exception + ) + ) + self.warc_input_failed.add(1) + return + else: + self.get_logger().info('Reading from S3 {}'.format(uri)) + # download entire file using a temporary file for buffering + warctemp = TemporaryFile(mode='w+b', dir=self.args.local_temp_dir) + try: + self.get_s3_client().download_fileobj(bucketname, path, warctemp) + warctemp.seek(0) + stream = warctemp + except botocore.client.ClientError as exception: + self.get_logger().error( + 'Failed to download {}: {}'.format(uri, exception) + ) + self.warc_input_failed.add(1) + warctemp.close() + + elif scheme == 'http' or scheme == 'https': + headers = None + if offset > -1 and length > 0: + headers = {'Range': 'bytes={}-{}'.format(offset, (offset + length - 1))} + # Note: avoid logging many small fetches + # self.get_logger().debug('Fetching {} ({})'.format(uri, headers)) + else: + self.get_logger().info('Fetching {}'.format(uri)) + response = requests.get(uri, headers=headers) + + if response.ok: + # includes "HTTP 206 Partial Content" for range requests + warctemp = SpooledTemporaryFile( + max_size=2097152, mode='w+b', dir=self.args.local_temp_dir + ) + warctemp.write(response.content) + warctemp.seek(0) + stream = warctemp + else: + self.get_logger().error( + 'Failed to download {}: {}'.format(uri, response.status_code) + ) + + elif scheme == 'hdfs': + try: + import pydoop.hdfs as hdfs + + self.get_logger().error('Reading from HDFS {}'.format(uri)) + stream = hdfs.open(uri) + except RuntimeError as exception: + self.get_logger().error('Failed to open {}: {}'.format(uri, exception)) + self.warc_input_failed.add(1) + + else: + self.get_logger().info('Reading local file {}'.format(uri)) + if scheme == 'file': + # must be an absolute path + uri = os.path.join('/', path) + else: + base_dir = os.path.abspath(os.path.dirname(__file__)) + uri = os.path.join(base_dir, uri) + try: + stream = open(uri, 'rb') + except IOError as exception: + self.get_logger().error('Failed to open {}: {}'.format(uri, exception)) + self.warc_input_failed.add(1) + + return stream + + def process_parquets(self, _id, iterator): + """Process parquet files, calling iterate_records(...) for each file""" + for uri in iterator: + self.warc_input_processed.add(1) + stream = self.fetch_parquet(uri, self.args.input_base_url) + if not stream: + continue + for res in self.process_parquet(uri, stream): + # print(f"res={res}") + yield res + stream.close() + + def process_parquet(self, uri, stream): + """Parse a Parquet""" + try: + # print(f"uri={uri}") + parquet_file = pq.ParquetFile(uri) + for res in self.iterate_records(uri, parquet_file): + yield res + except Exception as exception: + print(f"process_parquet exception={exception}") + self.warc_input_failed.add(1) + self.get_logger().error('Invalid Parquet: {} - {}'.format(uri, exception)) + + + def iterate_records(self, uri, parquet_file): + file_name=uri.split("/cc-index/")[-1] + # print(f"parquet_file={parquet_file}") + chunk_size = 1e6 + for idx, batch in enumerate(parquet_file.iter_batches(batch_size=chunk_size, columns=self.index_toread_cols)): + print(f"parquet {uri.split("/")[-1]} -> batch idx={idx}") + # print(f"#parquet_records={self.parquet_records.value}") + # print(f"#records_processed={self.records_processed.value}") + chunk_df = batch.to_pandas() + for row in chunk_df.itertuples(): + for res in self.process_record(row): + self.parquet_records.add(1) + url, url_host_name, content_languages, warc_filename, warc_record_offset, warc_record_length, warc_segment = res + if url_host_name: + self.records_processed.add(1) + yield (file_name,) + res + + def process_record(self, record): + # print(f"parquet_records={record}") + content_languages = record.content_languages + self.parquet_credibench_records.add(1) + if content_languages: + content_languages_lst = content_languages.split(",") + url,url_host_name,warc_filename,warc_record_offset,warc_record_length, warc_segment = None, None, None, None, None, None + # print(f"self.args.filter_by_supported_languages={self.args.filter_by_supported_languages}") + if not self.args.filter_by_supported_languages or (len(set(content_languages_lst) & set(self.supported_langs)) > 0): + url_host_name = record.url_host_name + if url_host_name in self.domains_pc1_dict.value: + url = record.url + warc_filename = record.warc_filename + warc_record_offset = record.warc_record_offset + warc_record_length = record.warc_record_length + warc_segment = record.warc_segment + else: + url_host_name = None + yield (url,url_host_name,content_languages,warc_filename,warc_record_offset,warc_record_length, warc_segment) + return (None,None, None, None, None, None, None) + + def init_accumulators(self, session): + super(Filter_CC_Index_Job, self).init_accumulators(session) + sc = session.sparkContext + self.records_failed = sc.accumulator(0) + self.parquet_records = sc.accumulator(0) + self.parquet_credibench_records = sc.accumulator(0) + + def log_accumulators(self, session): + super(Filter_CC_Index_Job, self).log_accumulators(session) + + self.log_accumulator(session, self.parquet_records, 'parquet_records = {}') + self.log_accumulator( + session, self.records_failed, 'records failed to process = {}' + ) + # self.log_accumulator(session, self.records_non_html, 'records not HTML = {}') + self.log_accumulator( + session, self.parquet_credibench_records, 'parquet_credibench_records= {}' + ) + @staticmethod + def load_domain_pc1(domains_pc1_csv_path="../../data/dqr/domain_pc1.csv"): + domains_df = pd.read_csv(domains_pc1_csv_path) + return dict(zip(domains_df["domain"].tolist(), domains_df["pc1"].tolist())) + def run_job(self, session): + self.get_logger().info(f"seed domain path={self.args.trusted_domains}") + out_path=str(session.conf.get("spark.sql.warehouse.dir")).split(":")[-1]+"/"+self.args.output + self.domains_pc1_dict = session.sparkContext.broadcast(self.load_domain_pc1(self.args.trusted_domains)) + # print(f"out_path={out_path}") + if os.path.exists(out_path): + # print(f"Exists={out_path}") + shutil.rmtree(out_path) + if self.args.input != '': + input_data = session.sparkContext.textFile( + # input_data=session.sparkContext.wholeTextFiles( + self.args.input, minPartitions=self.args.num_input_partitions + ) + output = input_data.mapPartitionsWithIndex(self.process_parquets) + if not self.args.intermediate_output: + df = session.createDataFrame(output, schema=self.output_schema) + else: + if output is not None: + session.createDataFrame(output, schema=self.output_schema).write.format( + self.args.output_format + ).option('compression', self.args.output_compression).saveAsTable( + self.args.intermediate_output + ) + self.log_accumulators(session.sparkContext) + warehouse_dir = session.conf.get( + 'spark.sql.warehouse.dir', 'spark-warehouse' + ) + intermediate_output = os.path.join( + warehouse_dir, self.args.intermediate_output + ) + df = session.read.parquet(intermediate_output) + df=df.dropDuplicates() + df = df.filter(df.warc_record_length >= 500) + w_desc = Window.partitionBy("url_host_name").orderBy(F.col("url_host_name").desc()) + w_asc = Window.partitionBy("url_host_name").orderBy(F.col("url_host_name").asc()) + df_low = (df + .withColumn("rn", F.row_number().over(w_asc)) + .filter(F.col("rn") <= 3) + .drop("rn") + ) + df_high = (df + .withColumn("rn", F.row_number().over(w_desc)) + .filter(F.col("rn") <= 3) + .drop("rn") + ) + df_final = df_low.union(df_high).distinct() + df_final = df_final.orderBy(F.col("url_host_name").asc(), F.col("warc_record_length").asc()) + # df_final.collect() + # df_sorted_Domain_Name_counts = df_final.groupBy("Domain_Name").count().collect() + df_final.coalesce(self.args.num_output_partitions).write.format(self.args.output_format).option( + 'compression', self.args.output_compression + ).mode("overwrite").saveAsTable(self.args.output) + self.log_accumulators(session.sparkContext) +if __name__ == '__main__': + job = Filter_CC_Index_Job() + # ExtractWetContentsJob.domains_pc1_dict=load_domain_pc1() + job.run() + + diff --git a/tgrag/cc-scripts/sparkcc.py b/tgrag/cc-scripts/sparkcc.py index 4bb6a96d..6b09dfe8 100644 --- a/tgrag/cc-scripts/sparkcc.py +++ b/tgrag/cc-scripts/sparkcc.py @@ -130,6 +130,11 @@ def parse_arguments(self): '--trusted_domains', help='The trusted domains pc1 .csv file to filter the WET file domains', ) + arg_parser.add_argument( + '--filter_by_supported_languages', + action='store_true', + help='Whether to filter_by_supported_languages or not', + ) self.add_arguments(arg_parser) args = arg_parser.parse_args() diff --git a/tgrag/cc-scripts/wet_extract_domain_content.py b/tgrag/cc-scripts/wet_extract_domain_content.py index 996fa5e2..614aae4d 100644 --- a/tgrag/cc-scripts/wet_extract_domain_content.py +++ b/tgrag/cc-scripts/wet_extract_domain_content.py @@ -6,6 +6,7 @@ from jsonpath_ng import jsonpath, parse as jsonpath_parse import os import shutil +import urllib import requests from pyspark.sql import Window from pyspark.sql.functions import row_number @@ -76,14 +77,12 @@ def process_record(self, record): if WARC_Identified_Content_Language: WARC_Identified_Content_Languages_lst = WARC_Identified_Content_Language.split(",") Domain_Name, WARC_Target_URI, WARC_Date, Content_Type, Content_Length, wet_record_txt = None, None, None, None, None, None - - if len(set(WARC_Identified_Content_Languages_lst) & set(self.supported_langs)) > 0: + # if len(set(WARC_Identified_Content_Languages_lst) & set(self.supported_langs)) > 0: + if 1 == 1: WARC_Target_URI = record.rec_headers['WARC-Target-URI'] - Domain_Name = urlparse(WARC_Target_URI).netloc - # FileName=input_file_name() - # print(f"FileName={FileName}") - if Domain_Name in self.domains_pc1_dict.value: - # if self.is_domain_exist(Domain_Name): + Domain_Name = urllib.parse.unquote(WARC_Target_URI.split("/")[2].split("www.")[-1].split("?")[0]) + if Domain_Name in self.domains_set.value: + # if self.is_domain_exist(Domain_Name): # print(f"{Domain_Name} exist") WARC_Date = record.rec_headers['WARC-Date'] Content_Type = record.rec_headers['Content-Type'] @@ -92,7 +91,8 @@ def process_record(self, record): else: # print(f"{Domain_Name} Not exist") Domain_Name = None - yield (Domain_Name, WARC_Target_URI, WARC_Identified_Content_Language, WARC_Date, Content_Type, Content_Length, wet_record_txt) + yield (Domain_Name, WARC_Target_URI, WARC_Identified_Content_Language, WARC_Date, Content_Type, + Content_Length, wet_record_txt) return (None, None, None, None, None, None, None) else: return (None, None, None, None, None, None, None) diff --git a/tgrag/content_embbeding/generate_content_embedding.py b/tgrag/content_embbeding/generate_content_embedding.py new file mode 100644 index 00000000..61f76b6d --- /dev/null +++ b/tgrag/content_embbeding/generate_content_embedding.py @@ -0,0 +1,58 @@ +import pandas as pd +from tqdm import tqdm +from huggingface_hub import hf_hub_download +from huggingface_hub import list_repo_files +from sentence_transformers import SentenceTransformer +import pyarrow.parquet as pq +import pickle +import argparse +def get_ds_files(repo_id,pattern="*.parquet"): + files = list_repo_files( + repo_id=repo_id, + repo_type="dataset") + files_lst=[] + for f in files: + if f.endswith(pattern[1:]): + files_lst.append(f) + return files_lst + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Content Embedding") + parser.add_argument("--hf_repo_id", type=str, default="Hussein-Abdallah/CrediBench-WebContent-Dec2024", help="dqr dataset path") + parser.add_argument("--emb_model", type=str, default="google/embeddinggemma-300m", help="LLM model Name", + choices=["Qwen/Qwen3-Embedding-0.6B", "Qwen/Qwen3-Embedding-8B", "Qwen/Qwen3-Embedding-4B", + "google/embeddinggemma-300m"]) + parser.add_argument("--local_dir", type=str, default="/home/mila/a/abdallah/hsh_projects/content_emb/content_emb", help="document key column") + parser.add_argument("--hf_files_start_idx", type=int, default=0, help="start file idx") + parser.add_argument("--hf_files_end_idx", type=int, default=100, help="end file idx") + parser.add_argument("--parquet_batch_size", type=int, default=100000, help="parquet file read rows batch_size") + parser.add_argument("--emb_batch_size", type=int, default=5000, help="GPU emb batch size") + parser.add_argument("--emb_dim", type=int, default=256, help="resize embedding dim") + args = parser.parse_args() + print(f"args={args}") + ds_name=args.hf_repo_id.split("/")[-1] + repo_parquet_files_lst = get_ds_files(args.hf_repo_id) + model = SentenceTransformer(args.emb_model) + for f_idx, f in tqdm(enumerate(repo_parquet_files_lst[args.hf_files_start_idx:args.hf_files_end_idx+1])): + hf_hub_download(repo_id=args.hf_repo_id, filename=f, repo_type="dataset", local_dir=f"{args.local_dir}/{ds_name}/") + # print(f"{local_dir}/{ds_name}/{f}") + parquet_file = pq.ParquetFile(f"{args.local_dir}/{ds_name}/{f}") + print(f"file={f}") + for i, batch in tqdm(enumerate(parquet_file.iter_batches(batch_size=args.parquet_batch_size)), + total=1 + int(parquet_file.metadata.num_rows / args.parquet_batch_size)): + df_chunk = batch.to_pandas() + urls_lst = df_chunk["WARC_Target_URI"].tolist() + domains_lst = df_chunk["Domain_Name"].tolist() + domains_txt = df_chunk["wet_record_txt"].astype(str).tolist() + embds = {} + for emb_batch_idx in tqdm(range(0, len(domains_lst), args.emb_batch_size)): + batch_emb = model.encode(domains_txt[emb_batch_idx:emb_batch_idx + args.emb_batch_size]) + for emb_idx in range(len(batch_emb)): + # print("domain_idx=",emb_batch_idx+emb_idx) + if domains_lst[emb_batch_idx + emb_idx] not in embds: + embds[domains_lst[emb_batch_idx + emb_idx]] = [] + embds[domains_lst[emb_batch_idx + emb_idx]].append( + [urls_lst[emb_batch_idx + emb_idx], batch_emb[emb_idx][0:args.emb_dim]]) + emb_file_name = f'{args.local_dir}/{f.split(".")[0]}_{f_idx}.pkl' + with open(emb_file_name, 'wb') as emb_file: + pickle.dump(embds, emb_file) diff --git a/tgrag/webScrape/scrape_home_pages.py b/tgrag/webScraper/scrape_home_pages.py similarity index 100% rename from tgrag/webScrape/scrape_home_pages.py rename to tgrag/webScraper/scrape_home_pages.py diff --git a/uv.lock b/uv.lock index b0bfd2be..73c7af45 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version < '3.10'", @@ -133,6 +133,29 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" }, ] +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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a/ExtraInfo/watfile_metadata.txt +++ b/ExtraInfo/watfile_metadata.txt @@ -1,5 +1,6 @@ Source: https://gist.github.com/pjox/4de9fb8962b715fe73bbe09d70b2e94e?h=1#file-saturn-pretty-wat + Metadata: { ###################### Generic MetaData ############## From c961b61dcf5f46fe015720590958e36290596d7b Mon Sep 17 00:00:00 2001 From: hussien Date: Wed, 14 Jan 2026 12:33:01 -0500 Subject: [PATCH 4/8] MLP full graph parallel Inference --- .gitignore | 8 + ExtraInfo/watfile_metadata.txt | 1 - bash_scripts/CC-Crawls/Jan2025.txt | 1 + bash_scripts/CC-Crawls/Mar2025.txt | 1 + bash_scripts/end-to-end-url.sh | 2 +- bash_scripts/end-to-end.sh | 24 +- bash_scripts/get_data.sh | 76 +- bash_scripts/job_content_ext.sh | 63 + bash_scripts/job_content_ext_sbatch10.sh | 21 + bash_scripts/job_index_build_sbatch.sh | 8 + bash_scripts/run_extract_wet_content.sh | 24 +- bash_scripts/run_filter_warc-index.sh | 36 +- bash_scripts/run_in_batches_warc-index.sh | 68 +- bash_scripts/run_in_batches_wet_content.sh | 12 +- pyproject.toml | 6 +- tgrag/cc-scripts/filter_cc_index.py | 14 +- tgrag/cc-scripts/sparkcc.py | 6 +- .../cc-scripts/wet_extract_domain_content.py | 9 +- .../generate_content_embedding.py | 2 +- .../mlp_experiments/job_mlp_infer.sh | 20 + .../mlp_experiments/mlp_inference.py | 178 + .../mlp_experiments/{main.py => mlp_train.py} | 132 +- uv.lock | 3072 ++++++++++++----- 23 files changed, 2880 insertions(+), 904 deletions(-) create mode 100644 bash_scripts/CC-Crawls/Jan2025.txt create mode 100644 bash_scripts/CC-Crawls/Mar2025.txt create mode 100644 bash_scripts/job_content_ext.sh create mode 100644 bash_scripts/job_content_ext_sbatch10.sh create mode 100644 bash_scripts/job_index_build_sbatch.sh create mode 100644 tgrag/experiments/mlp_experiments/job_mlp_infer.sh create mode 100644 tgrag/experiments/mlp_experiments/mlp_inference.py rename tgrag/experiments/mlp_experiments/{main.py => mlp_train.py} (89%) diff --git a/.gitignore b/.gitignore index 8915a15f..57a6cadf 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ #Ours data/ +plots/ results/ saved_graphs/ spark-warehouse/ @@ -93,6 +94,10 @@ docs/_build/ # PyBuilder .pybuilder/ target/ +# parquet files +*.parquet +# log files +*.log* # Jupyter Notebook .ipynb_checkpoints @@ -191,3 +196,6 @@ cython_debug/ # PyPI configuration file .pypirc +bash_scripts/jdk-25_linux-x64_bin.tar +bash_scripts/jdk-17.0.12_linux-x64_bin.tar.gz +bash_scripts/jdk-25_linux-x64_bin.tar.gz diff --git a/ExtraInfo/watfile_metadata.txt b/ExtraInfo/watfile_metadata.txt index 9a44e528..40909b23 100644 --- a/ExtraInfo/watfile_metadata.txt +++ b/ExtraInfo/watfile_metadata.txt @@ -1,6 +1,5 @@ Source: https://gist.github.com/pjox/4de9fb8962b715fe73bbe09d70b2e94e?h=1#file-saturn-pretty-wat - Metadata: { ###################### Generic MetaData ############## diff --git a/bash_scripts/CC-Crawls/Jan2025.txt b/bash_scripts/CC-Crawls/Jan2025.txt new file mode 100644 index 00000000..c01d4c69 --- /dev/null +++ b/bash_scripts/CC-Crawls/Jan2025.txt @@ -0,0 +1 @@ +CC-MAIN-2025-05 \ No newline at end of file diff --git a/bash_scripts/CC-Crawls/Mar2025.txt b/bash_scripts/CC-Crawls/Mar2025.txt new file mode 100644 index 00000000..94a9022b --- /dev/null +++ b/bash_scripts/CC-Crawls/Mar2025.txt @@ -0,0 +1 @@ +CC-MAIN-2025-13 \ No newline at end of file diff --git a/bash_scripts/end-to-end-url.sh b/bash_scripts/end-to-end-url.sh index f769c64f..a965a5c5 100755 --- a/bash_scripts/end-to-end-url.sh +++ b/bash_scripts/end-to-end-url.sh @@ -88,4 +88,4 @@ for data_type in "${cc_file_types[@]}" ; do fi echo "********************** End Of $data_type Task **********************" done -done < "$CRAWL_LIST_FILE" +done < "$CRAWL_LIST_FILE" \ No newline at end of file diff --git a/bash_scripts/end-to-end.sh b/bash_scripts/end-to-end.sh index a1d3b6d9..35ffb7d8 100755 --- a/bash_scripts/end-to-end.sh +++ b/bash_scripts/end-to-end.sh @@ -40,10 +40,18 @@ else spark_table_name=$6 fi + +if [ -z "$7" ]; then + listing=0 +else + listing=$7 +fi + echo "cc_file_types= ${cc_file_types[@]}" echo "start_idx=$start_idx end_idx=$end_idx" echo "seed_list=$seed_list" echo "spark_table_name=$spark_table_name" +echo "listing path=$listing" CRAWL_ARG="$1" @@ -72,8 +80,8 @@ for data_type in "${cc_file_types[@]}" ; do echo $CRAWL CRAWL_LowerCase=${CRAWL,,} echo "################################### run get data @ $(date '+%Y-%m-%d %H:%M:%S') ###################################" - echo "$SCRIPT_DIR/get_data.sh" "$CRAWL" $start_idx $end_idx "[$data_type]" - "$SCRIPT_DIR/get_data.sh" "$CRAWL" $start_idx $end_idx "[$data_type]" + echo "$SCRIPT_DIR/get_data.sh" "$CRAWL" $start_idx $end_idx "[$data_type]" $listing + "$SCRIPT_DIR/get_data.sh" "$CRAWL" $start_idx $end_idx "[$data_type]" $listing echo "Data Downloaded for $CRAWL." if [ "$data_type" = "wat" ]; then echo "################ Start Processing Processing $data_type Files ######################" @@ -90,20 +98,20 @@ for data_type in "${cc_file_types[@]}" ; do else rm -rf "$SCRATCH/spark-warehouse/wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running fi - echo "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" - "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" + echo "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" "$start_idx" "$end_idx" + "$SCRIPT_DIR/run_extract_wet_content.sh" "$CRAWL" "wet_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" "$start_idx" "$end_idx" echo "wet_extract_content_table constructed for $CRAWL batch_${start_idx}_${end_idx}" - elif [ "$data_type" = "cc-index-table" ]; then + elif [ "$data_type" = "cc-index-table" ]; then # collect content page wet-files from the CC Index (process only ~300 files ) echo "##################### run_filter_index @ $(date '+%Y-%m-%d %H:%M:%S') #####################" if [ -z "$SCRATCH" ]; then rm -rf "$PROJECT_ROOT/bash_scripts/spark-warehouse/cc-index-table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running else rm -rf "$SCRATCH/spark-warehouse/cc-index-table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}/" # Remove re-created directories before running fi - echo "$SCRIPT_DIR/run_filter_warc-index.sh" "$CRAWL" "warc_index_table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" - "$SCRIPT_DIR/run_filter_warc-index.sh" "$CRAWL" "warc_index_table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" + echo "$SCRIPT_DIR/run_filter_warc-index.sh" "$CRAWL" "warc_index_table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" "$start_idx" "$end_idx" + "$SCRIPT_DIR/run_filter_warc-index.sh" "$CRAWL" "warc_index_table_${spark_table_name}_${CRAWL_LowerCase//-/}_${start_idx}_${end_idx}" "$seed_list" "$start_idx" "$end_idx" echo "filter_warc-index table constructed for $CRAWL batch_${start_idx}_${end_idx}" fi echo "********************** End Of $data_type Task @ $(date '+%Y-%m-%d %H:%M:%S') **********************" done -done #< "$CRAWLS" +done \ No newline at end of file diff --git a/bash_scripts/get_data.sh b/bash_scripts/get_data.sh index 209a42dd..c3cae0b8 100755 --- a/bash_scripts/get_data.sh +++ b/bash_scripts/get_data.sh @@ -6,7 +6,7 @@ fetch_with_retries() { local tmp="${out}.part" i status sleep_time mkdir -p "$(dirname "$out")" for ((i=1; i<=tries; i++)); do - wget -c --tries=1 \ + wget -q -c --tries=1 \ --retry-connrefused \ --retry-on-http-error=429,500,502,503,504 \ --timeout=60 --read-timeout=60 \ @@ -43,7 +43,7 @@ else fi if [ -z "$3" ]; then - end_idx=30 + end_idx=10 else end_idx=$3 fi @@ -56,8 +56,16 @@ else IFS=',' read -ra cc_file_types <<< "$cleaned" # 2. Convert comma-separated string to array0 fi + +if [ -z "$5" ]; then + listing="0" +else + listing=$5 +fi + echo "cc_file_types= ${cc_file_types[@]}" echo "start_idx=$start_idx end_idx=$end_idx" +echo "listing path=$listing" # Get the root of the project (one level above this script's directory) SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" @@ -87,11 +95,43 @@ for data_type in "${cc_file_types[@]}" ; do echo "Downloading Common Crawl paths listings (${data_type} files of $CRAWL)..." mkdir -p "$DATA_DIR/crawl-data/$CRAWL/" - listing="$DATA_DIR/crawl-data/$CRAWL/$data_type.paths.gz" - cd "$DATA_DIR/crawl-data/$CRAWL/" - wget --timestamping "$BASE_URL/crawl-data/$CRAWL/$data_type.paths.gz" - sleep 2 - cd - + all_listing_content_path="$INPUT_DIR/${CRAWL}_test_all_${data_type}.txt" + echo "all_listing_content_path=$all_listing_content_path" + + if [[ "$listing" == "0" ]]; then + echo "listing is not provided" + listing="$DATA_DIR/crawl-data/$CRAWL/$data_type.paths.gz" + if [ -e "$all_listing_content_path" ]; then ## listing paths has been downloaded in previouse batches + echo "$all_listing_content_path exist." + listing_content=$(<"$all_listing_content_path") + else ## listing paths is to be downloaded + cd "$DATA_DIR/crawl-data/$CRAWL/" + wget -q --timestamping "$BASE_URL/crawl-data/$CRAWL/$data_type.paths.gz" + cd - + echo "Downloading ${data_type} file paths..." + file=$(gzip -dc "$listing" | head -1) + full_path="$DATA_DIR/$file" + mkdir -p "$(dirname "$full_path")" + cd "$(dirname "$full_path")" + wget -q --timestamping "$BASE_URL/$file" + cd - + input="$INPUT_DIR/all_${data_type}_${CRAWL}.txt" + echo "All ${data_type} files of ${CRAWL}: $input" + listing_content=$(gzip -dc "$listing") + if [ -e "$all_listing_content_path" ]; then + rm "$all_listing_content_path" + fi + echo "$listing_content" >>"$all_listing_content_path" + fi + else ############### Listing paths is given in a certian order (index) + echo "listing is provided" + listing_content=$(<"$listing") + # echo "$listing_content" + if [ -e "$all_listing_content_path" ]; then + rm "$all_listing_content_path" + fi + echo "$listing_content" >>"$all_listing_content_path" + fi echo "Downloading sample ${data_type} file..." # make sample fetch non-fatal so we reach the main loop even if it 503s @@ -99,18 +139,18 @@ for data_type in "${cc_file_types[@]}" ; do if [ -n "$file" ]; then full_path="$DATA_DIR/$file" mkdir -p "$(dirname "$full_path")" - ( cd "$(dirname "$full_path")" && wget --timestamping "$BASE_URL/$file" ) || \ + ( cd "$(dirname "$full_path")" && wget -q --timestamping "$BASE_URL/$file" ) || \ echo "[WARN] sample $data_type fetch failed; continuing" fi input="$INPUT_DIR/all_${data_type}_${CRAWL}.txt" echo "All ${data_type} files of ${CRAWL}: $input" - all_listing_content_path="$INPUT_DIR/test_all_${data_type}.txt" + all_listing_content_path="$INPUT_DIR/${CRAWL}_test_all_${data_type}.txt" if [[ "$data_type" = "cc-index-table" ]]; then listing_content=$(gzip -dc "$listing" | grep -F "/subset=warc/") - echo "cc-index-table listing_content=$listing_content" - else + # echo "cc-index-table listing_content=$listing_content" + elif ["$listing" = "0" ]; then listing_content=$(gzip -dc "$listing") fi @@ -120,13 +160,13 @@ for data_type in "${cc_file_types[@]}" ; do if [ "$listing_FilesCount" -lt "$end_idx" ] ; then end_idx=$listing_FilesCount fi + echo "end_idx=$end_idx" FilesCount=$((end_idx - start_idx + 1)) + input="$INPUT_DIR/${CRAWL}_test_${data_type}_${start_idx}_${end_idx}.txt" start_idx=$((start_idx + 1)) echo "To Process FilesCount=$FilesCount" - wat_files=$(echo "$listing_content" | tail -n +$start_idx | head -n $FilesCount) - echo "Writing input file listings..." - input="$INPUT_DIR/test_${data_type}.txt" + echo "Writing input file listings..." echo "Test file: $input" if [ -e "$input" ]; then rm "$input" @@ -151,17 +191,17 @@ for data_type in "${cc_file_types[@]}" ; do else first=$(echo "$wat_file" | awk -F '/'$data_type'/' '{print $1}') fi - echo "first=$first" + # echo "first=$first" file_path="$DATA_DIR/$wat_file" - echo "file_path=$file_path" + # echo "file_path=$file_path" if [[ "$data_type" = "cc-index-table" ]]; then target_dir="$DATA_DIR/$first/subset=warc/" else target_dir="$DATA_DIR/$first/$data_type/" fi - echo "target_dir=$target_dir" + # echo "target_dir=$target_dir" target_file="${target_dir}$(basename "$wat_file")" - echo "target_file=$target_file" + # echo "target_file=$target_file" if [ -f "$file_path" ] || [ -f "$target_file" ]; then # verify gzip; re-download if corrupt diff --git a/bash_scripts/job_content_ext.sh b/bash_scripts/job_content_ext.sh new file mode 100644 index 00000000..74cbf1fc --- /dev/null +++ b/bash_scripts/job_content_ext.sh @@ -0,0 +1,63 @@ +#!/bin/bash +#SBATCH --ntasks=1 +#SBATCH --partition=long-cpu +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=10 +#SBATCH --mem=64G +#SBATCH --time=24:00:00 +#SBATCH --output=/home/mila/a/abdallah/scratch/jobs_log/cc-content-ext/cc-content-ext_job_%j.out +#SBATCH --error=/home/mila/a/abdallah/scratch/jobs_log/cc-content-ext/cc-content-ext_job_%j.err + +# Exit on error +set -e + + +if [ -z "$1" ]; then + CRAWL=ccmain202508 +else + CRAWL=$1 +fi +CRAWL=${CRAWL,,} +wetFilesOrder="credigraph_${CRAWL}_wetFilesOrder.txt" +if [ -z "$2" ]; then + Month=Feb2025 +else + Month=$2 +fi + +if [ -z "$3" ]; then + sidx=0 +else + sidx=$3 +fi + +if [ -z "$4" ]; then + eidx=10 +else + eidx=$4 +fi + +if [ -z "$5" ]; then + batch_size=10 +else + batch_size=$5 +fi +# Echo time and hostname into log +echo "Date: $(date)" +echo "Hostname: $(hostname)" +echo "CRAWL: $CRAWL" +echo "Month: $Month" +echo "sidx: $sidx" +echo "eidx: $eidx" +echo "batch_size: $batch_size" +export JAVA_HOME=~/jdk-17.0.12/ +export PATH=$PATH:$JAVA_HOME/bin + +# Execute Python script +# Use `uv run --offline` on clusters without internet access on compute nodes. +for ((i=$sidx; i<$eidx; i+=$batch_size)); do + echo "#########################################################################################" + echo ./end-to-end.sh CC-Crawls/${Month}.txt $i $((i+$batch_size-1)) [wet] ../data/${Month}/${Month}_domains_${sidx}_${eidx}.csv content_ext_table spark-warehouse/$wetFilesOrder + ./end-to-end.sh CC-Crawls/${Month}.txt $i $((i+$batch_size-1)) [wet] ../data/${Month}/${Month}_domains_${sidx}_${eidx}.csv content_ext_table spark-warehouse/$wetFilesOrder + # rm -r ~/scratch/crawl-data/CC-MAIN-2025-08/segments +done \ No newline at end of file diff --git a/bash_scripts/job_content_ext_sbatch10.sh b/bash_scripts/job_content_ext_sbatch10.sh new file mode 100644 index 00000000..c46aa7fe --- /dev/null +++ b/bash_scripts/job_content_ext_sbatch10.sh @@ -0,0 +1,21 @@ +# CRAWL=ccmain202508 +# Month=Feb2025 + +CRAWL=ccmain202505 +Month=Jan2025 + +# CRAWL=ccmain202513 +# Month=Mar2025 + + + +batch_size=5000 +for ((i=50000; i<70000; i+=$batch_size)); do + if ((i >= 20000)); then + batch_size=10000 + fi + end=$(($i+$batch_size)) + echo sbatch ./job_content_ext.sh $CRAWL $Month $i $end 50 + sbatch ./job_content_ext.sh $CRAWL $Month $i $end 50 +done +# sbatch ./job_content_ext.sh ccmain202508 Feb2025 0 10000 50 \ No newline at end of file diff --git a/bash_scripts/job_index_build_sbatch.sh b/bash_scripts/job_index_build_sbatch.sh new file mode 100644 index 00000000..91d21277 --- /dev/null +++ b/bash_scripts/job_index_build_sbatch.sh @@ -0,0 +1,8 @@ +# CRAWL=ccmain202508 +CRAWL=None +# Month=Feb2025 +# Month=Jan2025 +Month=Mar2025 +batch_size=10 +echo sbatch ./run_in_batches_warc-index.sh $CRAWL $Month 240 300 $batch_size +sbatch ./run_in_batches_warc-index.sh $CRAWL $Month 240 300 $batch_size diff --git a/bash_scripts/run_extract_wet_content.sh b/bash_scripts/run_extract_wet_content.sh index 8ac70bb2..1d5ac901 100755 --- a/bash_scripts/run_extract_wet_content.sh +++ b/bash_scripts/run_extract_wet_content.sh @@ -23,7 +23,18 @@ else seed_list=$3 fi -echo "outputTableName=outputTableName" +if [ -z "$4" ]; then + start_idx=1 +else + start_idx=$4 +fi + +if [ -z "$5" ]; then + end_idx=10 +else + end_idx=$5 +fi +echo "outputTableName=$outputTableName" echo "seed_list=$seed_list" # Get the root of the project (one level above this script's directory) @@ -61,9 +72,16 @@ export PYSPARK_DRIVER_PYTHON="$VENV_PATH/bin/python" --executor-memory 5g \ --py-files "$PROJECT_ROOT/tgrag/cc-scripts/sparkcc.py" \ "$PROJECT_ROOT/tgrag/cc-scripts/wet_extract_domain_content.py" \ - "$INPUT_DIR/test_wet.txt" \ + "$INPUT_DIR/${CRAWL}_test_wet_${start_idx}_${end_idx}.txt" \ "$outputTableName" \ --trusted_domains "$seed_list" \ --output_format "parquet" \ --output_compression "snappy" \ - --log_level "WARN" \ No newline at end of file + --log_level "WARN" + + ################### delete processed batch files ############### + while IFS= read -r line; do + file_Path="${line##*:}" + echo "delete file path=$file_Path" + rm -f "$file_Path" + done < "$INPUT_DIR/${CRAWL}_test_wet_${start_idx}_${end_idx}.txt" \ No newline at end of file diff --git a/bash_scripts/run_filter_warc-index.sh b/bash_scripts/run_filter_warc-index.sh index e4a459f8..ee5d773a 100755 --- a/bash_scripts/run_filter_warc-index.sh +++ b/bash_scripts/run_filter_warc-index.sh @@ -1,3 +1,4 @@ + #!/bin/bash # Fail on first error @@ -12,7 +13,7 @@ fi CRAWL="$1" if [ -z "$2" ]; then - outputTableName="filter_warcindex_table" + outputTableName="wat_extract_content_table" else outputTableName="$2" fi @@ -22,6 +23,19 @@ if [ -z "$3" ]; then else seed_list=$3 fi + +if [ -z "$4" ]; then + start_idx=1 +else + start_idx=$4 +fi + +if [ -z "$5" ]; then + end_idx=10 +else + end_idx=$5 +fi +echo "outputTableName=$outputTableName" echo "seed_list=$seed_list" # Get the root of the project (one level above this script's directory) @@ -49,19 +63,23 @@ export PYSPARK_DRIVER_PYTHON="$VENV_PATH/bin/python" # Run the Spark job # Local testing: use "$INPUT_DIR/test_wat.txt" -# Cluster / full usage: ""$INPUT_DIR/all_wat_$CRAWL.txt" -# --trusted_domains "../data/dqr/domain_pc1.csv"\ -# --trusted_domains "../common_urls_set_84k.csv" \ -# "$PROJECT_ROOT/tgrag/cc-scripts/wet_extract_domain_content_11k.py" +# XX:+UseG1GC -> explicitly tells the JVM to use the Garbage-First (G1) Garbage Collector echo "INPUT_DIR=$INPUT_DIR" "$VENV_PATH/bin/spark-submit" \ - --driver-memory 10g \ - --executor-memory 5g \ + --driver-memory 15g \ + --executor-memory 10g \ --py-files "$PROJECT_ROOT/tgrag/cc-scripts/sparkcc.py" \ "$PROJECT_ROOT/tgrag/cc-scripts/filter_cc_index.py" \ - "$INPUT_DIR/test_cc-index-table.txt" \ + "$INPUT_DIR/${CRAWL}_test_cc-index-table_${start_idx}_${end_idx}.txt" \ "$outputTableName" \ --trusted_domains "$seed_list" \ --output_format "parquet" \ --output_compression "snappy" \ - --log_level "WARN" \ No newline at end of file + --log_level "WARN" + + ################### delete processed batch files ############### + while IFS= read -r line; do + file_Path="${line##*:}" + echo "delete file path=$file_Path" + rm -f "$file_Path" + done < "$INPUT_DIR/${CRAWL}_test_cc-index-table_${start_idx}_${end_idx}.txt" \ No newline at end of file diff --git a/bash_scripts/run_in_batches_warc-index.sh b/bash_scripts/run_in_batches_warc-index.sh index d22cf8d6..57e02bac 100755 --- a/bash_scripts/run_in_batches_warc-index.sh +++ b/bash_scripts/run_in_batches_warc-index.sh @@ -1,11 +1,63 @@ #!/bin/bash +#SBATCH --ntasks=1 +#SBATCH --partition=long-cpu +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=20 +#SBATCH --mem=128G +#SBATCH --time=10:00:00 +#SBATCH --output=/home/mila/a/abdallah/scratch/jobs_log/cc-index-build/cc-index-build_job_%j.out +#SBATCH --error=/home/mila/a/abdallah/scratch/jobs_log/cc-index-build/cc-index-build_job_%j.err + +# Exit on error set -e -batch_size=2 -data_type="cc-index-table" -for ((i=0; i<300; i+=$batch_size)); do - echo "#########################################################################################" - echo "/end-to-end.sh CC-Crawls/Feb2025.txt $i $((i+batch_size-1)) [$data_type]" - ./end-to-end.sh CC-Crawls/Feb2025.txt $i $((i+batch_size-1)) [$data_type] - rm -r ../data/cc-index/table/cc-main/warc/crawl=CC-MAIN-2025-08/subset=warc -done + +if [ -z "$1" ]; then + CRAWL=ccmain202508 +else + CRAWL=$1 +fi +CRAWL=${CRAWL,,} +if [ -z "$2" ]; then + Month=Feb2025 +else + Month=$2 +fi + +if [ -z "$3" ]; then + sidx=0 +else + sidx=$3 +fi + +if [ -z "$4" ]; then + eidx=10 +else + eidx=$4 +fi + +if [ -z "$5" ]; then + batch_size=10 +else + batch_size=$5 +fi +# Echo time and hostname into log +echo "Date: $(date)" +echo "Hostname: $(hostname)" +echo "CRAWL: $CRAWL" +echo "Month: $Month" +echo "sidx: $sidx" +echo "eidx: $eidx" +echo "batch_size: $batch_size" +export JAVA_HOME=~/jdk-17.0.12/ +export PATH=$PATH:$JAVA_HOME/bin + +# Execute Python script +# Use `uv run --offline` on clusters without internet access on compute nodes. + +for ((i=$sidx; i<$eidx; i+=$batch_size)); do + echo "#########################################################################################" + echo "/end-to-end.sh CC-Crawls/$Month.txt $i $((i+batch_size-1)) [cc-index-table] ../data/${Month}/${Month}_domains.csv" + ./end-to-end.sh CC-Crawls/$Month.txt $i $((i+batch_size-1)) [cc-index-table] ../data/${Month}/${Month}_domains.csv + # rm -r ~/scratch/cc-index/table/cc-main/warc/crawl=CC-MAIN-2025-08/subset=warc +done \ No newline at end of file diff --git a/bash_scripts/run_in_batches_wet_content.sh b/bash_scripts/run_in_batches_wet_content.sh index a11cda09..9e661a15 100755 --- a/bash_scripts/run_in_batches_wet_content.sh +++ b/bash_scripts/run_in_batches_wet_content.sh @@ -1,12 +1,14 @@ #!/bin/bash set -e -for ((i=0; i<90001; i+=100)); do +batch_size=25 +for ((i=25; i<90001; i+=$batch_size)); do echo "#########################################################################################" - echo "/end-to-end-content.sh CC-Crawls/Dec2024txt $i $((i+99)) [wet]" - ./end-to-end.sh CC-Crawls/Dec2024.txt $i $((i+99)) [wet] - rm -r ../data/crawl-data/CC-MAIN-2024-42/segments + echo "/end-to-end-content.sh CC-Crawls/Feb2025 $i $((i+$batch_size-1)) [wet] ../data/Entire-Graph_Nov-2024/credigraph_Nov2024_wetFilesOrder.txt" + ./end-to-end.sh CC-Crawls/Feb2025.txt $i $((i+$batch_size-1)) [wet] ../data/Feb2025/Feb2025_domains.csv content_table spark-warehouse/credigraph_ccmain202508_wetFilesOrder.txt + rm -r ~/scratch/crawl-data/CC-MAIN-2025-08/segments + # rm -r ../data/crawl-data/CC-MAIN-2024-42/segments # rm -r ../data/crawl-data/CC-MAIN-2024-46/segments # rm -r ../data/crawl-data/CC-MAIN-2024-51/segments - rm -r /shared_mnt/tmp/* + # rm -r /shared_mnt/tmp/* done diff --git a/pyproject.toml b/pyproject.toml index 07fe128d..7e482cd2 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 = ">=3.11" authors = [ ] dependencies = [ @@ -16,7 +16,7 @@ dependencies = [ "pyyaml>=6.0.2", "requests>=2.32.3", "scikit-learn>=1.6.1", - "scipy>=1.13.1", + "scipy>=1.16.0", "sentence-transformers>=5.0.0", "tldextract>=5.3.0", "torch>=2.7.1", @@ -33,6 +33,8 @@ dependencies = [ "pandas", "pyarrow", "huggingface-hub", + "jsonpath_ng", + "vllm", ] [tool.flit.module] diff --git a/tgrag/cc-scripts/filter_cc_index.py b/tgrag/cc-scripts/filter_cc_index.py index fc935575..5b93da68 100644 --- a/tgrag/cc-scripts/filter_cc_index.py +++ b/tgrag/cc-scripts/filter_cc_index.py @@ -38,7 +38,7 @@ class Filter_CC_Index_Job(CCSparkJob): parquet_records = None parquet_credibench_records = None records_failed = None - domains_pc1_dict=None + domains_set=None supported_langs = ["eng", "fra"] # "language codes: ISO-639-3 " filter_by_languages=False @@ -212,7 +212,7 @@ def process_record(self, record): # print(f"self.args.filter_by_supported_languages={self.args.filter_by_supported_languages}") if not self.args.filter_by_supported_languages or (len(set(content_languages_lst) & set(self.supported_langs)) > 0): url_host_name = record.url_host_name - if url_host_name in self.domains_pc1_dict.value: + if url_host_name in self.domains_set.value: url = record.url warc_filename = record.warc_filename warc_record_offset = record.warc_record_offset @@ -247,8 +247,13 @@ def load_domain_pc1(domains_pc1_csv_path="../../data/dqr/domain_pc1.csv"): return dict(zip(domains_df["domain"].tolist(), domains_df["pc1"].tolist())) def run_job(self, session): self.get_logger().info(f"seed domain path={self.args.trusted_domains}") - out_path=str(session.conf.get("spark.sql.warehouse.dir")).split(":")[-1]+"/"+self.args.output - self.domains_pc1_dict = session.sparkContext.broadcast(self.load_domain_pc1(self.args.trusted_domains)) + out_path=str(session.conf.get("spark.sql.warehouse.dir")).split(":")[-1]+"/"+self.args.output + cc_label_deg_3_df=pd.read_csv(self.args.trusted_domains) + # cc_label_deg_3_df.columns=["domain"] + cc_label_deg_3_set=set(cc_label_deg_3_df["domain"].tolist()) + del cc_label_deg_3_df + self.domains_set = session.sparkContext.broadcast(cc_label_deg_3_set) + del cc_label_deg_3_set # print(f"out_path={out_path}") if os.path.exists(out_path): # print(f"Exists={out_path}") @@ -300,7 +305,6 @@ def run_job(self, session): self.log_accumulators(session.sparkContext) if __name__ == '__main__': job = Filter_CC_Index_Job() - # ExtractWetContentsJob.domains_pc1_dict=load_domain_pc1() job.run() diff --git a/tgrag/cc-scripts/sparkcc.py b/tgrag/cc-scripts/sparkcc.py index 6b09dfe8..18cbeb0a 100644 --- a/tgrag/cc-scripts/sparkcc.py +++ b/tgrag/cc-scripts/sparkcc.py @@ -207,12 +207,14 @@ def run(self): # .config("spark.executor.instances", str(os.cpu_count()/4)) # Number of executor JVMs (workers) .config("spark.executor.cores", "4") .config("spark.executor.instances", "8") - .config("spark.executor.memory", "2g") - .config("spark.driver.memory", "5g") + .config("spark.executor.memory", "8g") + .config("spark.yarn.executor.memoryOverhead", "2g") + .config("spark.driver.memory", "10g") .config("spark.hadoop.dfs.block.size", "256m") .config("spark.sql.files.maxPartitionBytes", "256mb") .config("spark.ui.host", "0.0.0.0") .config("spark.ui.port", "4040") + .config("spark.driver.extraJavaOptions", "-XX:+UseG1GC") ) if self.args.spark_profiler: diff --git a/tgrag/cc-scripts/wet_extract_domain_content.py b/tgrag/cc-scripts/wet_extract_domain_content.py index 614aae4d..d2297bef 100644 --- a/tgrag/cc-scripts/wet_extract_domain_content.py +++ b/tgrag/cc-scripts/wet_extract_domain_content.py @@ -133,7 +133,14 @@ def load_domain_pc1(domains_pc1_csv_path="../../data/dqr/domain_pc1.csv"): def run_job(self, session): self.get_logger().info(f"seed domain path={self.args.trusted_domains}") out_path=str(session.conf.get("spark.sql.warehouse.dir")).split(":")[-1]+"/"+self.args.output - self.domains_pc1_dict = session.sparkContext.broadcast(self.load_domain_pc1(self.args.trusted_domains)) + + cc_label_deg_3_df=pd.read_csv(self.args.trusted_domains) + cc_label_deg_3_set=set(cc_label_deg_3_df["domain"].tolist()) + del cc_label_deg_3_df + self.domains_set = session.sparkContext.broadcast(cc_label_deg_3_set) + del cc_label_deg_3_set + + # self.domains_pc1_dict = session.sparkContext.broadcast(self.load_domain_pc1(self.args.trusted_domains)) if os.path.exists(out_path): shutil.rmtree(out_path) if self.args.input != '': diff --git a/tgrag/content_embbeding/generate_content_embedding.py b/tgrag/content_embbeding/generate_content_embedding.py index 61f76b6d..205e8efc 100644 --- a/tgrag/content_embbeding/generate_content_embedding.py +++ b/tgrag/content_embbeding/generate_content_embedding.py @@ -22,7 +22,7 @@ def get_ds_files(repo_id,pattern="*.parquet"): parser.add_argument("--emb_model", type=str, default="google/embeddinggemma-300m", help="LLM model Name", choices=["Qwen/Qwen3-Embedding-0.6B", "Qwen/Qwen3-Embedding-8B", "Qwen/Qwen3-Embedding-4B", "google/embeddinggemma-300m"]) - parser.add_argument("--local_dir", type=str, default="/home/mila/a/abdallah/hsh_projects/content_emb/content_emb", help="document key column") + parser.add_argument("--local_dir", type=str, default="/home/mila/a/abdallah/hsh_projects/content_emb/content_emb", help="document key column") parser.add_argument("--hf_files_start_idx", type=int, default=0, help="start file idx") parser.add_argument("--hf_files_end_idx", type=int, default=100, help="end file idx") parser.add_argument("--parquet_batch_size", type=int, default=100000, help="parquet file read rows batch_size") diff --git a/tgrag/experiments/mlp_experiments/job_mlp_infer.sh b/tgrag/experiments/mlp_experiments/job_mlp_infer.sh new file mode 100644 index 00000000..da5cd7c1 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/job_mlp_infer.sh @@ -0,0 +1,20 @@ +#!/bin/bash +#SBATCH --ntasks=1 +#SBATCH --partition=long-cpu +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=16 +#SBATCH --mem=64G +#SBATCH --time=24:00:00 +#SBATCH --output=/home/mila/a/abdallah/scratch/jobs_log/mlp_infer_job_%j.out +#SBATCH --error=/home/mila/a/abdallah/scratch/jobs_log/mlp_infer_job_%j.err + +# Exit on error +set -e + +# Echo time and hostname into log +echo "Date: $(date)" +echo "Hostname: $(hostname)" + +# Execute Python script +# Use `uv run --offline` on clusters without internet access on compute nodes. +uv run python mlp_inference.py \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/mlp_inference.py b/tgrag/experiments/mlp_experiments/mlp_inference.py new file mode 100644 index 00000000..8ae469a7 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/mlp_inference.py @@ -0,0 +1,178 @@ +import argparse +import logging +from typing import Dict, cast +import pickle +import numpy as np +from tgrag.utils.args import parse_args +from tgrag.utils.logger import setup_logging +from tgrag.utils.path import get_root_dir +from tqdm import tqdm +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.neural_network import MLPRegressor +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from statistics import mean +import pyarrow.parquet as pq +import pyarrow.compute as pc +import multiprocessing +from multiprocessing import Pool, cpu_count +import threading + +# shard_domains_dict={} +# def process_chunk(chunk): +# global shard_domains_dict +# for k, v in chunk: +# shard_domains_dict[v].append(k) +text_emb_shards_dict={} +domain_index_text_dict, domain_index_set={},() +domain_index_gnn_dict, index_domain_gnn_dict={},{} +def load_emb_index(path="../../../data/Dec2024/gnn_random_v0", index_pickle="domain_shard_index.pkl",invert=True ): + with open(f'{path}/{index_pickle}', 'rb') as f: + index_dict = pickle.load(f) + # global shard_domains_dict + shard_domains_dict=None + if invert: + shard_domains_dict={} + val_set=set(index_dict.values()) + for v in val_set: + shard_domains_dict[v]=[] + + for k,v in index_dict.items(): + shard_domains_dict[v].append(k) + return index_dict,shard_domains_dict + +def load_emb_dict(path="../../../data/Dec2024/gnn_random_v0", emb_pickle="domain_shard_index.pkl"): + shared_key=emb_pickle.split('.')[0] + if shared_key not in text_emb_shards_dict: + with open(f'{path}/{emb_pickle}', 'rb') as f: + embd_dict = pickle.load(f) + if isinstance(embd_dict[list(embd_dict.keys())[0]], list): + embd_dict={ k:v[0][1] for k,v in embd_dict.items()} + text_emb_shards_dict[shared_key]=embd_dict + return text_emb_shards_dict[shared_key] + +def search_parquet_gnn(path="../../../data/Dec2024/gnn_random_v0", parquet_name="shard_0.parquet", column_name="",search_values=[]): + column_name = 'domain' + filters = [(column_name, 'in', search_values)] + table = pq.read_table(f'{path}/{parquet_name}', filters=filters) + emb_dict={} + for batch in table.to_batches(): + for i in range(len(batch)): + emb_dict[str(batch['domain'][i])]=batch['emb'][i].as_py() + return emb_dict +def search_parquet_content(path="../../../data/Dec2024/gnn_random_v0", parquet_name="shard_0.parquet", column_name="",search_values=[]): + column_name = 'domain' + filters = [(column_name, 'in', search_values)] + table = pq.read_table(f'{path}/{parquet_name}', filters=filters) + emb_dict={} + for batch in table.to_batches(): + for i in range(len(batch)): + emb_dict[str(batch['domain'][i])]=batch['embeddings'][i][0]['emb'].as_py() ## first doc emb + return emb_dict + +def process_inference_batch(idx,mlp_reg,batch_domains,gnn_emb_path,text_emb_path,gnn_emb_idx): + # batch_domains = gnn_v[i:i + batch_size] + print(f"started: gnn_emb_idx={gnn_emb_idx}\t batch_idx={idx}") + global index_domain_text_dict,domain_index_set + gnn_emb_dict=search_parquet_gnn(gnn_emb_path, parquet_name=f"shard_{gnn_emb_idx}.parquet", column_name="domain",search_values=batch_domains) + text_emb_index_batch_dict={} + text_emb_index_batch_dict[-1]=[] ## domains without text content emb + for k in domain_index_set: + text_emb_index_batch_dict[k]=[] + + for k in gnn_emb_dict: + if k not in domain_index_text_dict: + domain_index_text_dict[k]=-1 + text_emb_index_batch_dict[domain_index_text_dict[k]].append(k) + + batch_scores_dict={} + for text_emb_shard,domains in tqdm(text_emb_index_batch_dict.items()): + if text_emb_shard==-1: + text_emb_dict={domain:[0]*256 for domain in domains} + elif len(domains)>0: + text_emb_dict=search_parquet_content(text_emb_path, parquet_name=f"{text_emb_shard}.parquet", column_name="domain",search_values=domains) + else: + continue + for domain in domains: + gnn_emb_dict[domain].extend(text_emb_dict[domain]) + + final_emb_dict={} + final_emb_dict=gnn_emb_dict + if len(final_emb_dict)>0: + pred_scores=mlp_reg.predict(list(final_emb_dict.values())) + socres_dict=dict(zip(list(final_emb_dict.keys()), list(pred_scores))) + batch_scores_dict.update(socres_dict) + # print(f"finished: gnn_emb_idx={gnn_emb_idx}\t batch_idx={idx}\ttext_emb_shard{text_emb_shard}") + print(f"finished: gnn_emb_idx={gnn_emb_idx}\t batch_idx={idx}") + return (batch_scores_dict,idx) +def result_callback(result): + print(f"\nCallback result received for batch idx={result[1]}\n") + +def main() -> None: + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP Inference") + parser.add_argument("--text_emb_path", type=str, default=str("/home/mila/a/abdallah/scratch/hsh_projects/content_emb/content_emb/nov2024_wetcontent_embeddinggemma-300m/Credibench_nov2024_wetcontent_embeddinggemma-300m/") ,help="text emb files path") + parser.add_argument("--gnn_emb_path", type=str, default=str(root + "/data/Nov2024/gnn_random/") ,help="gnn emb files path") + parser.add_argument("--model_path", type=str, default=str("/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/plots/"),help="model path") + parser.add_argument("--month", type=str, default="nov", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--exec_mode", type=str, default="multiprocess", choices=["serial", "multithread", "multiprocess"],help="execution mode") + args = parser.parse_args() + print("args=", args) + ############## Load Model ############### + modelname = f"mlp_model_{args.month}_pc1_text_embeddinggemma-300m_GNN-RNI.pkl" + mlp_reg=None + with open(f'{args.model_path}/{modelname}', 'rb') as f: + mlp_reg = pickle.load(f) + X_test_feat = [[0]*256*2] + pred = mlp_reg.predict(X_test_feat) + ############## Load emb index ############### + global domain_index_text_dict, domain_index_set + global domain_index_gnn_dict, index_domain_gnn_dict + domain_index_text_dict, domain_index_set = load_emb_index(args.text_emb_path, f"{args.month}2024_wetcontent_domains_index.pkl",False) + domain_index_set=set(domain_index_text_dict.values()) + domain_index_gnn_dict, index_domain_gnn_dict = load_emb_index(args.gnn_emb_path, "domain_shard_index.pkl",True) + batch_size=int(4e5) + print(f'batch_size={batch_size}') + print(f'cpu_count={cpu_count()}') + for gnn_k,gnn_v in tqdm(index_domain_gnn_dict.items()): + # if int(gnn_k) in [0,2,3,4,5,7,8,9,10,11,12,13,15,16]: + # print(f"Skipping shard:{gnn_k}") + # continue + ############### serial Execution ############### + print(f"shared {gnn_k} len of domains={len(gnn_v)}") + if args.exec_mode=="serial": + results=[] + for i in tqdm(range(0, len(gnn_v), batch_size)): + results.append(process_inference_batch(i,mlp_reg,gnn_v[i:i + batch_size],args.gnn_emb_path,args.text_emb_path,gnn_k)) + ############### parallel Execution ############### + elif args.exec_mode == "multiprocess": + data=[(i,mlp_reg,gnn_v[i:i + batch_size],args.gnn_emb_path,args.text_emb_path,gnn_k) for i in range(0, len(gnn_v), batch_size)] + print(f"Total number of batches={len(data)}") + ########## Multiprocessing Pool ############### + with multiprocessing.Pool(processes=8) as pool: + results = pool.starmap(process_inference_batch, data) + # print(results) + pool.close() + ########### Multithreading ############### + elif args.exec_mode == "multithread": + data=[(i,mlp_reg,gnn_v[i:i + batch_size],args.gnn_emb_path,args.text_emb_path,gnn_k) for i in range(0, len(gnn_v), batch_size)] + threads = [] + for d in data: + threads.append(threading.Thread(target=process_inference_batch, args=d)) + for t in threads: + t.start() + for t in threads: + t.join() + ########### Save Results############### + batch_infer_dict={} + for res in results: + batch_infer_dict.update(res[0]) + results=None + pd.DataFrame(list(zip(batch_infer_dict.keys(), batch_infer_dict.values())),columns=["domain", "pc1_score"])\ + .to_parquet(f"{args.model_path}/mlpInfer_{args.month}2024_pc1_embeddinggemma-300m_GNN-RNI_shard_{gnn_k}.parquet", + engine='pyarrow',row_group_size=50_000,use_dictionary=True,compression="snappy") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/main.py b/tgrag/experiments/mlp_experiments/mlp_train.py similarity index 89% rename from tgrag/experiments/mlp_experiments/main.py rename to tgrag/experiments/mlp_experiments/mlp_train.py index d8605a49..937637d3 100644 --- a/tgrag/experiments/mlp_experiments/main.py +++ b/tgrag/experiments/mlp_experiments/mlp_train.py @@ -15,15 +15,20 @@ from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error from statistics import mean from mlp_multihead import MultiTaskMLP, train_multihead - +from sklearn.preprocessing import normalize +def normalize_embeddings(emb_dict): + norm_arr=normalize(list(emb_dict.values())) + return {k: norm_arr[idx] for idx,k in enumerate(emb_dict.keys())} def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", - month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg",normalize=False): months_emb_lst = [] if embed_type == "GNN_GAT": for month in month_lst: with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: embd_dict = pickle.load(f) + if normalize: + embd_dict=normalize_embeddings(embd_dict) months_emb_lst.append(embd_dict) common_domains_set = set(months_emb_lst[0].keys()).intersection(months_emb_lst[1].keys()).intersection( months_emb_lst[2].keys()) @@ -75,10 +80,10 @@ def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", m return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] -def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1"): +def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=256,normalize=False): if embed_type == "text": if model_name == "embeddinggemma-300m": - with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_768.pkl', 'rb') as f: + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: embd_dict = pickle.load(f) elif model_name == "embeddingQwen3-0.6B": with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: @@ -90,8 +95,7 @@ def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-3 with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: embd_dict = pickle.load(f) elif model_name == "IPTC_Topic_emb": - with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl', - 'rb') as f: + with open(f'IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl','rb') as f: embd_dict = pickle.load(f) elif embed_type == "domainName": with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: @@ -99,18 +103,16 @@ def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-3 elif embed_type == "GNN_GAT": # with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: # embd_dict=pickle.load(f) - with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: embd_dict = pickle.load(f) elif embed_type == "IPTC_Topic": - with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_IPTC-news-topic_scores.pkl', 'rb') as f: + with open(f'IPTCTopicModeling/dqr_IPTC-news-topic_scores.pkl', 'rb') as f: embd_dict = pickle.load(f) elif embed_type == "IPTC_Topic_freq": - with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_topics_frequancy_norm_dict.pkl', - 'rb') as f: + with open(f'IPTCTopicModeling/dqr_topics_frequancy_norm_dict.pkl','rb') as f: embd_dict = pickle.load(f) elif embed_type == "IPTC_Topic_emb": - with open(f'/shared_mnt/github_repos/CrediGraph/IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl', - 'rb') as f: + with open(f'IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl','rb') as f: embd_dict = pickle.load(f) # print(list(embd_dict.keys())[0],embd_dict[list(embd_dict.keys())[0]]) elif embed_type == "3Feat": @@ -142,7 +144,12 @@ def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-3 elif embed_type == "PASTEL_hasContent": with open(f'{path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: embd_dict = pickle.load(f) - + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) return embd_dict @@ -365,44 +372,16 @@ def eval(pred, true): return mse, mae, r2, mean_mae, min_diff_dict, max_diff_dict -def main() -> None: - root = str(get_root_dir()) - parser = argparse.ArgumentParser(description="MLP Experiments") - parser.add_argument("--target", type=str, default="pc1", choices=["pc1", "mbfc", "mbfc_bias"], help="the credability target") - parser.add_argument("--emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") - parser.add_argument("--dqr_path", type=str, default=str(root + "/data/dqr"),help="dqr dataset path") - parser.add_argument("--embed_type", type=str, default="text", - choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") - parser.add_argument("--emb_model", type=str, default="embeddingTE3L", - choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", - "IPTC_Topic_emb"],help="LLM embedding model") - parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") - parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") - parser.add_argument("--emb_dim", type=int, default=4096,help="embedding size") - parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") - # parser.add_argument("--lr", type=float, default=3e-5) - parser.add_argument("--lr", type=float, default=5e-3,help="learning rate") - parser.add_argument("--epochs", type=int,default=14,help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L - parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") - parser.add_argument("--runs", type=int, default=1,help="# training runs") - parser.add_argument("--use_gnn_emb", type=bool, default=False,help="append GNN embedding") - parser.add_argument("--agg_month_emb", type=bool, default=False, help="aggregate montly GNN embeddings") - parser.add_argument("--use_topic_emb", type=bool, default=False,help="use topic modeling features") - parser.add_argument("--filter_by_GNN_nodes", type=bool, default=True,help="filter by domains has GNN embeddings") - parser.add_argument("--filter_by_PASTEL_domains", type=bool, default=False,help="filter by domains has PASTEL embeddings") - parser.add_argument("--MultiHead", type=bool, default=True, help="Use MultiHead Model") - parser.add_argument("--num_classes", type=int, default=3,help="# classifcation classes for Multihead model") - parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") - parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") - args = parser.parse_args() - print("args=", args) +def mlp_train(args) -> None: ############## Load training data and split ############### - text_emb_dict = load_emb_dict(args.embed_type, args.emb_path, args.emb_model, args.month) + text_emb_dict = load_emb_dict(args.embed_type, args.text_emb_path, args.emb_model, args.month,normalize=True) labeled_11k_df = pd.read_csv(f"{args.dqr_path}/domain_ratings.csv") + targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") + targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(text_emb_dict)] + labeled_11k_df = labeled_11k_df.reset_index(drop=True) ############### filter by the 8K GNN Nodes ################### - if args.filter_by_GNN_nodes: - targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") - targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + if args.filter_by_GNN_nodes: labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(targets_nodes_df["domain_rev"])] labeled_11k_df = labeled_11k_df.reset_index(drop=True) if args.filter_by_PASTEL_domains: @@ -414,8 +393,7 @@ def main() -> None: ################# Train ##################### results = [] for i in range(args.runs): - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, labeled_11k_df, - args.test_valid_size) + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, labeled_11k_df,args.test_valid_size) if args.embed_type == "TFIDF": print(f"TFIDF dim={text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]}") X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, X_test, @@ -426,13 +404,11 @@ def main() -> None: if args.use_gnn_emb: if args.agg_month_emb: gnn_emb_dict = load_agg_Nmonth_emb_dict("GNN_GAT", args.emb_path, agg="avg") - labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(gnn_emb_dict.keys())] - labeled_11k_df = labeled_11k_df.reset_index(drop=True) - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, - labeled_11k_df, - args.test_valid_size) else: - gnn_emb_dict = load_emb_dict("GNN_GAT", args.emb_path) + gnn_emb_dict = load_emb_dict("GNN_GAT", args.gnn_emb_path,month=args.month) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(gnn_emb_dict.keys())] + labeled_11k_df = labeled_11k_df.reset_index(drop=True) + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target,labeled_11k_df,args.test_valid_size) else: gnn_emb_dict = None if args.use_topic_emb: @@ -442,7 +418,7 @@ def main() -> None: # topic_iptc_emb_dict = load_emb_dict("3Feat", args.emb_path) # topic_iptc_emb_dict = load_emb_dict("3Feat2", args.emb_path) # gnn_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) - topic_iptc_emb_dict = load_emb_dict("PASTEL_hasContent", args.emb_path) + topic_iptc_emb_dict = load_emb_dict("PASTEL_hasContent", args.text_emb_path) else: topic_iptc_emb_dict = None X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, @@ -472,15 +448,19 @@ def main() -> None: epochs=args.epochs, w_reg=0.5, w_cls=0.5, num_classes=args.num_classes) + ###################### Save Model #################### + filename = f"{args.plots_out_path}/mlp_model_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}.pkl" + with open(filename, 'wb') as file: + pickle.dump(mlp_reg, file) ################## Weak Supervision ########### if args.generate_weaksupervision_scores == True: if args.agg_month_emb: embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = load_weaksupervision_emb_dict( - args.embed_type, args.emb_path, args.emb_model, month="dec", target=args.target, + args.embed_type, args.text_emb_path, args.emb_model, month="dec", target=args.target, gnn_emb=args.use_gnn_emb, agg="avg") else: embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = load_weaksupervision_emb_dict( - args.embed_type, args.emb_path, args.emb_model, month="dec", target=args.target, + args.embed_type, args.text_emb_path, args.emb_model, month="dec", target=args.target, gnn_emb=args.use_gnn_emb, agg=None) phishtank_features = [v for k, v in embd_dict_phishtank.items()] @@ -536,4 +516,36 @@ def main() -> None: if __name__ == '__main__': - main() \ No newline at end of file + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP Experiments") + parser.add_argument("--target", type=str, default="pc1", choices=["pc1", "mbfc", "mbfc_bias"], help="the credability target") + parser.add_argument("--text_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--gnn_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--dqr_path", type=str, default=str(root + "/data/dqr"),help="dqr dataset path") + parser.add_argument("--embed_type", type=str, default="text", + choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") + parser.add_argument("--emb_model", type=str, default="embeddingTE3L", + choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", + "IPTC_Topic_emb"],help="LLM embedding model") + parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") + parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") + parser.add_argument("--emb_dim", type=int, default=4096,help="embedding size") + parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") + # parser.add_argument("--lr", type=float, default=3e-5) + parser.add_argument("--lr", type=float, default=5e-3,help="learning rate") + # parser.add_argument("--lr", type=float, default=5e-4) + parser.add_argument("--epochs", type=int,default=8,help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L + parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") + parser.add_argument("--runs", type=int, default=1,help="# training runs") + parser.add_argument("--use_gnn_emb", type=bool, default=True,help="append GNN embedding") + parser.add_argument("--agg_month_emb", type=bool, default=False, help="aggregate montly GNN embeddings") + parser.add_argument("--use_topic_emb", type=bool, default=False,help="use topic modeling features") + parser.add_argument("--filter_by_GNN_nodes", type=bool, default=True,help="filter by domains has GNN embeddings") + parser.add_argument("--filter_by_PASTEL_domains", type=bool, default=False,help="filter by domains has PASTEL embeddings") + parser.add_argument("--MultiHead", type=bool, default=False, help="Use MultiHead Model") + parser.add_argument("--num_classes", type=int, default=3,help="# classifcation classes for Multihead model") + parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") + parser.add_argument("--month", type=str, default="nov", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + args = parser.parse_args() + print("args=", args) + mlp_train(args) \ No newline at end of file diff --git a/uv.lock b/uv.lock index 73c7af45..8d3dc7e8 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,9 @@ version = 1 revision = 3 -requires-python = ">=3.9" +requires-python = ">=3.11" resolution-markers = [ - "python_full_version < '3.10'", - "python_full_version == '3.10.*'", - "python_full_version == '3.11.*'", "python_full_version >= '3.12'", + "python_full_version < 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"https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, -] From afd7c191cb283d65d899f7e9bdb4b574bd1d7b7c Mon Sep 17 00:00:00 2001 From: hussien Date: Fri, 23 Jan 2026 16:19:17 -0500 Subject: [PATCH 5/8] MLP inference + DuckDB search +,Pytorch Regressor --- .gitignore | 1 + pyproject.toml | 1 + .../mlp_experiments/mlp_inference.py | 8 +- .../mlp_experiments/mlp_modules.py | 240 ++++++++++++++++++ .../mlp_experiments/mlp_multihead.py | 109 -------- .../experiments/mlp_experiments/mlp_train.py | 227 +++-------------- tgrag/experiments/mlp_experiments/utils.py | 141 ++++++++++ tgrag/webScraper/scrape_home_pages.py | 8 +- uv.lock | 38 +++ 9 files changed, 471 insertions(+), 302 deletions(-) create mode 100644 tgrag/experiments/mlp_experiments/mlp_modules.py delete mode 100644 tgrag/experiments/mlp_experiments/mlp_multihead.py create mode 100644 tgrag/experiments/mlp_experiments/utils.py diff --git a/.gitignore b/.gitignore index 57a6cadf..370f7882 100644 --- a/.gitignore +++ b/.gitignore @@ -199,3 +199,4 @@ cython_debug/ bash_scripts/jdk-25_linux-x64_bin.tar bash_scripts/jdk-17.0.12_linux-x64_bin.tar.gz bash_scripts/jdk-25_linux-x64_bin.tar.gz +uv.lock diff --git a/pyproject.toml b/pyproject.toml index 7e482cd2..6837364d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,7 @@ dependencies = [ "huggingface-hub", "jsonpath_ng", "vllm", + "duckdb", ] [tool.flit.module] diff --git a/tgrag/experiments/mlp_experiments/mlp_inference.py b/tgrag/experiments/mlp_experiments/mlp_inference.py index 8ae469a7..6e921783 100644 --- a/tgrag/experiments/mlp_experiments/mlp_inference.py +++ b/tgrag/experiments/mlp_experiments/mlp_inference.py @@ -137,9 +137,9 @@ def main() -> None: print(f'batch_size={batch_size}') print(f'cpu_count={cpu_count()}') for gnn_k,gnn_v in tqdm(index_domain_gnn_dict.items()): - # if int(gnn_k) in [0,2,3,4,5,7,8,9,10,11,12,13,15,16]: - # print(f"Skipping shard:{gnn_k}") - # continue + if int(gnn_k) not in [3,6,9]: + print(f"Skipping shard:{gnn_k}") + continue ############### serial Execution ############### print(f"shared {gnn_k} len of domains={len(gnn_v)}") if args.exec_mode=="serial": @@ -151,7 +151,7 @@ def main() -> None: data=[(i,mlp_reg,gnn_v[i:i + batch_size],args.gnn_emb_path,args.text_emb_path,gnn_k) for i in range(0, len(gnn_v), batch_size)] print(f"Total number of batches={len(data)}") ########## Multiprocessing Pool ############### - with multiprocessing.Pool(processes=8) as pool: + with multiprocessing.Pool(processes=4) as pool: results = pool.starmap(process_inference_batch, data) # print(results) pool.close() diff --git a/tgrag/experiments/mlp_experiments/mlp_modules.py b/tgrag/experiments/mlp_experiments/mlp_modules.py new file mode 100644 index 00000000..1902f1dc --- /dev/null +++ b/tgrag/experiments/mlp_experiments/mlp_modules.py @@ -0,0 +1,240 @@ +# ---------------------------- +# Required imports +# ---------------------------- +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, TensorDataset +import numpy as np +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from tqdm import tqdm +import random +import copy + +# ---------------------------- +seed = 42 +torch.manual_seed(seed) +random.seed(seed) + +def train_scikitlearn_regressor(mlp_reg, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, epochs=15): + batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] + early_stopper = Sklearn_EarlyStopping(patience=10) + for _ in tqdm(range(epochs)): + for b in range(0, len(Y_train), batch_size): + X_batch, y_batch = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] + batch_mean = sum(y_batch) / len(y_batch) + mlp_reg.partial_fit(X_batch, y_batch) + train_loss.append(mlp_reg.loss_) + valid_loss.append(mean_squared_error(Y_valid, mlp_reg.predict(X_valid_feat))) + test_loss.append(mean_squared_error(Y_test, mlp_reg.predict(X_test_feat))) + mean_loss.append(mean_squared_error(y_batch, [batch_mean for elem in y_batch])) + if early_stopper.step(valid_loss[-1], mlp_reg): + print("Early stopping triggered.") + mlp_reg = early_stopper.restore_best_weights() + break + return mlp_reg, train_loss, valid_loss, test_loss, mean_loss + +class EarlyStopping: + def __init__(self, patience=10, min_delta=0.0): + self.patience = patience + self.min_delta = min_delta + self.best_loss = float("inf") + self.counter = 0 + self.best_state = None + + def step(self, val_loss, model): + if val_loss < self.best_loss - self.min_delta: + self.best_loss = val_loss + self.counter = 0 + self.best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} + return False + else: + self.counter += 1 + return self.counter >= self.patience + + def restore_best_weights(self, model): + model.load_state_dict(self.best_state) +class Sklearn_EarlyStopping: + def __init__(self, patience=10, min_delta=0.0): + self.patience = patience + self.min_delta = min_delta + self.best_loss = float("inf") + self.counter = 0 + self.best_state = None + + def step(self, val_loss, model): + if val_loss < self.best_loss - self.min_delta: + self.best_loss = val_loss + self.counter = 0 + self.best_state = copy.deepcopy(model) + return False + else: + self.counter += 1 + return self.counter >= self.patience + + def restore_best_weights(self): + return self.best_state + +class MLP3LayersPredictor(nn.Module): + def __init__( + self, in_dim: int, hidden_dim_multiplier: float = 0.5, out_dim: int = 1 + ): + super().__init__() + hidden_dim = int(hidden_dim_multiplier * in_dim) + self.lin_node = nn.Linear(in_dim, hidden_dim) + self.lin_node2 = nn.Linear(hidden_dim, hidden_dim//2) + self.lin_node3 = nn.Linear(hidden_dim//2, hidden_dim//8) + self.out = nn.Linear(hidden_dim//8, out_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.lin_node(x) + x = x.relu() + x = self.lin_node2(x) + x = x.relu() + x = self.lin_node3(x) + x = x.relu() + x = self.out(x) + x = x.sigmoid() + return x + def predict(self, x): + return self.forward(torch.tensor(x).float()).detach().squeeze(1).numpy() + +class MLPRegressor(nn.Module): + def __init__(self, input_size, hidden_layer_sizes): + super(MLPRegressor, self).__init__() + layers = [] + layers.append(nn.Linear(input_size, hidden_layer_sizes[0])) + layers.append(nn.ReLU()) + # Hidden layers + for i in range(len(hidden_layer_sizes) - 1): + layers.append(nn.Linear(hidden_layer_sizes[i], hidden_layer_sizes[i+1])) + layers.append(nn.ReLU()) + layers.append(nn.Linear(hidden_layer_sizes[-1], 1)) + layers.append(nn.Sigmoid()) + self.model = nn.Sequential(*layers) + def forward(self, x): + return self.model(x) + + def predict(self, x): + return self.forward(torch.tensor(x).float()).detach().squeeze(1).numpy() +def train_mlp(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15): + batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion_reg = nn.MSELoss() + early_stopper = EarlyStopping(patience=10) + for epoch in tqdm(range(epochs)): + for b in range(0, len(Y_train), batch_size): + X_batch, y_reg = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] + batch_mean = sum(y_reg) / len(y_reg) + reg_out= model(torch.tensor(X_batch).float()) + # Loss calculations + loss_reg = criterion_reg(reg_out.squeeze(1), torch.tensor(y_reg)) + # Weighted multi-task loss + train_loss_val = loss_reg + train_loss.append(train_loss_val.detach().numpy()) + val_reg_out = model(torch.tensor(X_valid_feat).float()) + valid_loss.append(criterion_reg(val_reg_out.detach().squeeze(1), torch.tensor(Y_valid)) ) + test_reg_out = model(torch.tensor(X_test_feat).float()) + test_loss.append(criterion_reg(test_reg_out.detach().squeeze(1), torch.tensor(Y_test))) + mean_loss.append(mean_squared_error(y_reg, [batch_mean for elem in y_reg])) + if epochs>(epoch+1): + train_loss_val.backward() + optimizer.step() + if early_stopper.step(valid_loss[-1], model): + print("Early stopping triggered.") + early_stopper.restore_best_weights(model) + break + return model, train_loss, valid_loss, test_loss, mean_loss + + +class MultiTaskMLP(nn.Module): + def __init__(self, input_dim, num_classes, hidden_dims=[128, 64]): + super(MultiTaskMLP, self).__init__() + + # Shared feature extractor + layers = [] + in_dim = input_dim + for h in hidden_dims: + layers.append(nn.Linear(in_dim, h)) + layers.append(nn.ReLU()) + # layers.append(nn.Dropout(0.3)) + # layers.append(nn.BatchNorm1d(h)) + in_dim = h + self.trunk = nn.Sequential(*layers) + + # Task-specific heads + self.reg_head = nn.Linear(in_dim, 1) # Regression + self.cls_head = nn.Linear(in_dim, num_classes) # Multi-class classification + + def forward(self, x): + z = self.trunk(x) + reg_out = self.reg_head(z) # (batch, 1) + cls_logits = self.cls_head(z) # (batch, num_classes) + cls_pred = torch.argmax(cls_logits, dim=1) + return reg_out, cls_logits, cls_pred + + def predict(self, x): + reg_out, cls_logits, cls_pred = self.forward(torch.tensor(x)) + return reg_out.detach().squeeze(1).numpy() + + +def train_multihead(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15, + num_classes=3, w_reg=0.8, w_cls=0.2): + batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion_reg = nn.MSELoss() + criterion_cls = nn.CrossEntropyLoss() + w_reg = w_reg + w_cls = w_cls + for epoch in tqdm(range(epochs)): + for b in range(0, len(Y_train), batch_size): + X_batch, y_reg = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] + y_cls = torch.tensor([int(((elem * 10) % 10) / (num_classes + 1)) for elem in y_reg], dtype=torch.long) + batch_mean = sum(y_reg) / len(y_reg) + reg_out, cls_logits, cls_pred = model(torch.tensor(X_batch)) + # Loss calculations + loss_reg = criterion_reg(reg_out.squeeze(1), torch.tensor(y_reg)) + loss_cls = criterion_cls(cls_logits, y_cls) + # Weighted multi-task loss + train_loss_val = w_reg * loss_reg + w_cls * loss_cls + train_loss.append(train_loss_val.detach().numpy()) + val_reg_out, val_cls_logits, val_cls_pred = model(torch.tensor(X_valid_feat)) + valid_loss.append(criterion_reg(val_reg_out.detach().squeeze(1), torch.tensor(Y_valid)) * w_reg) + test_reg_out, test_cls_logits, test_cls_pred = model(torch.tensor(X_test_feat)) + test_loss.append(criterion_reg(test_reg_out.detach().squeeze(1), torch.tensor(Y_test)) * w_reg) + mean_loss.append(mean_squared_error(y_reg, [batch_mean for elem in y_reg])) + if epochs>(epoch+1): + train_loss_val.backward() + optimizer.step() + return model, train_loss, valid_loss, test_loss, mean_loss + + +def train_multihead_v0(model, epochs=100): + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + criterion_reg = nn.MSELoss() + criterion_cls = nn.CrossEntropyLoss() + # Task weights + w_reg = 0.4 + w_cls = 0.6 + # Dummy batch + x = torch.randn(32, 100) + y_reg = torch.tensor(np.arange(0, 32), dtype=torch.float64) + y_cls = torch.tensor([int(elem) % 2 for elem in y_reg], dtype=torch.long) + y_reg = y_reg.reshape(32, 1).float() + for i in range(epochs): + # Forward pass + reg_out, cls_logits, cls_pred = model(x) + # Loss calculations + loss_reg = criterion_reg(reg_out, y_reg) + loss_cls = criterion_cls(cls_logits, y_cls) + print(f"loss_reg={loss_reg}\tcls_logits={cls_logits}") + # Weighted multi-task loss + loss = w_reg * loss_reg + w_cls * loss_cls + print(loss) + loss.backward() + optimizer.step() + +if __name__ == '__main__': + model = MultiTaskMLP(input_dim=100, num_classes=2, hidden_dims=[1024, 256, 32]) + train_multihead_v0(model, 100) + diff --git a/tgrag/experiments/mlp_experiments/mlp_multihead.py b/tgrag/experiments/mlp_experiments/mlp_multihead.py deleted file mode 100644 index 9fa51a9f..00000000 --- a/tgrag/experiments/mlp_experiments/mlp_multihead.py +++ /dev/null @@ -1,109 +0,0 @@ -# ---------------------------- -# Required imports -# ---------------------------- -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader, TensorDataset -import numpy as np -from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error -from tqdm import tqdm -import random - -# ---------------------------- -seed = 42 -torch.manual_seed(seed) -random.seed(seed) - - -class MultiTaskMLP(nn.Module): - def __init__(self, input_dim, num_classes, hidden_dims=[128, 64]): - super(MultiTaskMLP, self).__init__() - - # Shared feature extractor - layers = [] - in_dim = input_dim - for h in hidden_dims: - layers.append(nn.Linear(in_dim, h)) - layers.append(nn.ReLU()) - # layers.append(nn.Dropout(0.3)) - # layers.append(nn.BatchNorm1d(h)) - in_dim = h - self.trunk = nn.Sequential(*layers) - - # Task-specific heads - self.reg_head = nn.Linear(in_dim, 1) # Regression - self.cls_head = nn.Linear(in_dim, num_classes) # Multi-class classification - - def forward(self, x): - z = self.trunk(x) - reg_out = self.reg_head(z) # (batch, 1) - cls_logits = self.cls_head(z) # (batch, num_classes) - cls_pred = torch.argmax(cls_logits, dim=1) - return reg_out, cls_logits, cls_pred - - def predict(self, x): - reg_out, cls_logits, cls_pred = self.forward(torch.tensor(x)) - return reg_out.detach().squeeze(1).numpy() - - -def train_multihead(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15, - num_classes=3, w_reg=0.8, w_cls=0.2): - batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] - optimizer = torch.optim.Adam(model.parameters(), lr=lr) - criterion_reg = nn.MSELoss() - criterion_cls = nn.CrossEntropyLoss() - w_reg = w_reg - w_cls = w_cls - for epoch in tqdm(range(epochs)): - for b in range(0, len(Y_train), batch_size): - X_batch, y_reg = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] - y_cls = torch.tensor([int(((elem * 10) % 10) / (num_classes + 1)) for elem in y_reg], dtype=torch.long) - batch_mean = sum(y_reg) / len(y_reg) - reg_out, cls_logits, cls_pred = model(torch.tensor(X_batch)) - # Loss calculations - loss_reg = criterion_reg(reg_out.squeeze(1), torch.tensor(y_reg)) - loss_cls = criterion_cls(cls_logits, y_cls) - # Weighted multi-task loss - train_loss_val = w_reg * loss_reg + w_cls * loss_cls - train_loss.append(train_loss_val.detach().numpy()) - val_reg_out, val_cls_logits, val_cls_pred = model(torch.tensor(X_valid_feat)) - valid_loss.append(criterion_reg(val_reg_out.detach().squeeze(1), torch.tensor(Y_valid)) * w_reg) - test_reg_out, test_cls_logits, test_cls_pred = model(torch.tensor(X_test_feat)) - test_loss.append(criterion_reg(test_reg_out.detach().squeeze(1), torch.tensor(Y_test)) * w_reg) - mean_loss.append(mean_squared_error(y_reg, [batch_mean for elem in y_reg])) - if epochs>(epoch+1): - train_loss_val.backward() - optimizer.step() - return model, train_loss, valid_loss, test_loss, mean_loss - - -def train_multihead_v0(model, epochs=100): - optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) - criterion_reg = nn.MSELoss() - criterion_cls = nn.CrossEntropyLoss() - # Task weights - w_reg = 0.4 - w_cls = 0.6 - # Dummy batch - x = torch.randn(32, 100) - y_reg = torch.tensor(np.arange(0, 32), dtype=torch.float64) - y_cls = torch.tensor([int(elem) % 2 for elem in y_reg], dtype=torch.long) - y_reg = y_reg.reshape(32, 1).float() - for i in range(epochs): - # Forward pass - reg_out, cls_logits, cls_pred = model(x) - # Loss calculations - loss_reg = criterion_reg(reg_out, y_reg) - loss_cls = criterion_cls(cls_logits, y_cls) - print(f"loss_reg={loss_reg}\tcls_logits={cls_logits}") - # Weighted multi-task loss - loss = w_reg * loss_reg + w_cls * loss_cls - print(loss) - loss.backward() - optimizer.step() - -if __name__ == '__main__': - model = MultiTaskMLP(input_dim=100, num_classes=2, hidden_dims=[1024, 256, 32]) - train_multihead_v0(model, 100) - diff --git a/tgrag/experiments/mlp_experiments/mlp_train.py b/tgrag/experiments/mlp_experiments/mlp_train.py index 937637d3..73687466 100644 --- a/tgrag/experiments/mlp_experiments/mlp_train.py +++ b/tgrag/experiments/mlp_experiments/mlp_train.py @@ -1,4 +1,5 @@ import argparse +import sklearn import logging from typing import Dict, cast import pickle @@ -9,16 +10,16 @@ from tqdm import tqdm import pandas as pd import matplotlib.pyplot as plt -from sklearn.neural_network import MLPRegressor +from sklearn.neural_network import MLPRegressor as Sklearn_MLPRegressor +from mlp_modules import MLPRegressor +from mlp_modules import MLP3LayersPredictor from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error from statistics import mean -from mlp_multihead import MultiTaskMLP, train_multihead +from mlp_modules import MultiTaskMLP, train_multihead,train_mlp,train_scikitlearn_regressor from sklearn.preprocessing import normalize -def normalize_embeddings(emb_dict): - norm_arr=normalize(list(emb_dict.values())) - return {k: norm_arr[idx] for idx,k in enumerate(emb_dict.keys())} +from utils import normalize_embeddings,train_valid_test_split,resize_emb,plot_histogram,plot_loss,plot_regression_scatter,eval def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month_lst=["dec", "nov", "oct"], target="pc1", agg="avg",normalize=False): @@ -228,153 +229,9 @@ def load_weaksupervision_emb_dict(embed_type, path="../../../data", model_name=" return embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit - -def train_valid_test_split(target, labeled_11k_df, test_valid_size=0.4): - if target == "mbfc_bias": - quantiles = labeled_11k_df[target].quantile([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) - else: - quantiles = labeled_11k_df[target].quantile([0.2, 0.4, 0.6, 0.8, 1.0]) - bins = [labeled_11k_df[target].min()] + quantiles.tolist() - labeled_11k_df[target + '_cat'] = pd.cut(labeled_11k_df[target], bins=bins, labels=quantiles, include_lowest=True) - X = labeled_11k_df[['domain', target]] - y = labeled_11k_df[target + '_cat'] - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=test_valid_size, stratify=y, random_state=42 - ) - X_valid, X_test, y_valid, y_test = train_test_split( - X_test, y_test, test_size=0.5, stratify=y_test, random_state=42) - return X_train, labeled_11k_df.iloc[X_train.index][target].tolist(), X_valid, labeled_11k_df.iloc[X_valid.index][ - target].tolist(), X_test, labeled_11k_df.iloc[X_test.index][target].tolist() - - -def resize_emb(text_emb, target, X_train, X_valid, X_test, gnn_emb=None, topic_emb=None, trim_to=1024): - X_train_feat = [text_emb[d][0:trim_to] for d in X_train["domain"].tolist()] - X_valid_feat = [text_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] - X_test_feat = [text_emb[d][0:trim_to] for d in X_test["domain"].tolist()] - print("emb-size=", len(X_test_feat[0])) - if gnn_emb is not None: - X_train_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_train["domain"].tolist()] - X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_train_feat_gnn, X_train_feat)] - - X_valid_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] - X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_valid_feat_gnn, X_valid_feat)] - - X_test_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_test["domain"].tolist()] - X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_gnn, X_test_feat)] - print("emb-size=", len(X_test_feat[0])) - if topic_emb is not None: - X_train_feat_topic = [topic_emb[d] for d in X_train["domain"].tolist()] - X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in - zip(X_train_feat_topic, X_train_feat)] - - X_valid_feat_topic = [topic_emb[d] for d in X_valid["domain"].tolist()] - X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in - zip(X_valid_feat_topic, X_valid_feat)] - - X_test_feat_topic = [topic_emb[d] for d in X_test["domain"].tolist()] - X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_topic, X_test_feat)] - print("emb-size=", len(X_test_feat[0])) - - return X_train_feat, X_valid_feat, X_test_feat - - -def train_mlp(mlp_reg, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, epochs=15): - batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] - for _ in tqdm(range(epochs)): - for b in range(0, len(Y_train), batch_size): - X_batch, y_batch = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] - batch_mean = sum(y_batch) / len(y_batch) - mlp_reg.partial_fit(X_batch, y_batch) - train_loss.append(mlp_reg.loss_) - valid_loss.append(mean_squared_error(Y_valid, mlp_reg.predict(X_valid_feat))) - test_loss.append(mean_squared_error(Y_test, mlp_reg.predict(X_test_feat))) - mean_loss.append(mean_squared_error(y_batch, [batch_mean for elem in y_batch])) - return mlp_reg, train_loss, valid_loss, test_loss, mean_loss - - -def plot_loss(train_loss, valid_loss, test_loss, mean_loss, out_path="../../visualizations", target="pc1", - embed_type="text", emb_model="", use_gnn_emb=False, use_topic_emb=False, run=1, month="dec"): - plt.figure(figsize=(5, 4)) - plt.rc('font', size=16) - plt.plot(range(len(train_loss)), train_loss, label="train loss") - plt.plot(range(len(train_loss)), valid_loss, label="validation loss") - plt.plot(range(len(train_loss)), test_loss, label="test loss") - plt.plot(range(len(train_loss)), mean_loss, label="mean loss") - plt.xticks(range(0, len(train_loss) + 1, 1 if len(train_loss) <= 10 else len(train_loss) // 10)) - plt.xlabel("Epoch") - plt.ylabel("MSE") - plt.grid(True) - plt.legend(fontsize=14) - plt.savefig( - f"{out_path}/dqr_{month}_{target}_{embed_type}_{emb_model}_{'+GNN-RNI' if use_gnn_emb else ''}_run{str(run)}_loss_128-200-15-60-20-00.pdf", - bbox_inches='tight', pad_inches=0.1) - plt.show() - - -def plot_histogram(true, pred, out_path="../../visualizations", target="pc1", embed_type="text", emb_model="", - use_gnn_emb=False, use_topic_emb=False, run=1, month="dec"): - plt.figure(figsize=(5, 4)) - plt.hist(pred, bins=50, range=(0, 1), edgecolor='black', color='lightblue', label="Pred") - plt.hist(true, bins=50, range=(0, 1), edgecolor='black', color='orange', alpha=0.6, label="True") - y_max = max(np.histogram(true, bins=50, range=(0, 1))[0].max(), - np.histogram(pred, bins=50, range=(0, 1))[0].max()) - plt.rc('font', size=15) - plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') - plt.yticks(np.arange(0, y_max + 50, 100), rotation=0, ha='right') - plt.xlabel(target.upper().replace("_", "-")) - plt.ylabel('Frequancy') - plt.legend() - plt.savefig( - f"{out_path}/dqr_{month}_{target}_{embed_type}_{emb_model}_{'GNN-RNI' if use_gnn_emb else ''}_{'topic-emb' if use_topic_emb else ''}_{'topic-emb' if use_topic_emb else ''}_run{str(run)}_testset_true_vs_pred_frequancy.pdf", - bbox_inches='tight', pad_inches=0.1) - plt.show() - - -def plot_regression_scatter(true, pred, out_path="../../visualizations", target="pc1", embed_type="text", emb_model="", - use_gnn_emb=False, use_topic_emb=False, run=1, month="dec"): - plt.figure(figsize=(5, 4)) - plt.rc('font', size=16) - plt.scatter(true, pred, alpha=0.7) - plt.plot([0, 1], [0, 1], color='red', linestyle='-', label='regression line') - plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') - plt.yticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') - plt.xlabel(target.upper().replace("_", "-")) - plt.ylabel(f"Predicted {target.upper().replace('_', '-')}") - plt.legend() - plt.grid(True) - plt.savefig( - f"{out_path}/dqr_{month}_{target}_{embed_type}_{emb_model}_{'GNN-RNI' if use_gnn_emb else ''}_{'topic-emb' if use_topic_emb else ''}_run{str(run)}_testset_true_vs_pred_scatter.pdf", - bbox_inches='tight', pad_inches=0.1) - plt.show() - - -def eval(pred, true): - max(abs(x - y) for x, y in zip(true, pred)) - res_df = pd.DataFrame(zip(true, pred), columns=['true', 'pred']) - res_df["diff"] = res_df.apply(lambda row: abs(row['true'] - row['pred']), axis=1) - max_idx = res_df.idxmax()["diff"] - min_idx = res_df.idxmin()["diff"] - max_diff_row = res_df.iloc[max_idx] - min_diff_row = res_df.iloc[min_idx] - max_diff_dict = max_diff_row.to_dict() - max_diff_dict["test_idx"] = max_idx - min_diff_dict = min_diff_row.to_dict() - min_diff_dict["test_idx"] = min_idx - - mse = mean_squared_error(true, pred) - # print(f"mse={mse}") - r2 = r2_score(true, pred) - # print(f"r2={r2}") - mae = mean_absolute_error(true, pred) - true_mean = mean(true) - mean_mae = mean_absolute_error(true, [true_mean for elem in true]) - # print(f"MAE={mae}") - return mse, mae, r2, mean_mae, min_diff_dict, max_diff_dict - - def mlp_train(args) -> None: ############## Load training data and split ############### - text_emb_dict = load_emb_dict(args.embed_type, args.text_emb_path, args.emb_model, args.month,normalize=True) + text_emb_dict = load_emb_dict(args.embed_type, args.text_emb_path, args.emb_model, args.month,normalize=False) labeled_11k_df = pd.read_csv(f"{args.dqr_path}/domain_ratings.csv") targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) @@ -432,16 +289,26 @@ def mlp_train(args) -> None: print(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") if args.MultiHead == False: - # mlp_reg = MLPRegressor(hidden_layer_sizes=(128, 64), activation='relu', solver='adam', max_iter=args.max_iter,random_state=42, verbose=False, learning_rate_init=args.lr,alpha=0.001) - mlp_reg = MLPRegressor(hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16), - activation='relu', solver='adam', max_iter=args.max_iter, random_state=42, - verbose=False, learning_rate_init=args.lr) - mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_mlp(mlp_reg, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, epochs=args.epochs) + ###################### PYTorch Regressor ###################### + if args.library=="pytorch": + mlp_reg = MLP3LayersPredictor(len(X_train_feat[0])) + # mlp_reg = MLPRegressor(len(X_train_feat[0]),hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16)) + mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_mlp(mlp_reg, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + ######################## Scikit-Learn ################### + elif args.library=="sklearn": + mlp_reg = Sklearn_MLPRegressor(hidden_layer_sizes=(128, 32), + activation='relu', solver='adam',max_iter=args.max_iter, random_state=42, + verbose=False, learning_rate_init=args.lr) + mlp_reg.out_activation_ = 'sigmoid' + mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_scikitlearn_regressor(mlp_reg, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + else: mlp_reg = MultiTaskMLP(input_dim=len(X_train_feat[0]), num_classes=args.num_classes, - hidden_dims=[int(len(X_train_feat[0]) * 0.9), 64, 16]) + hidden_dims=[int(len(X_train_feat[0]) * 0.9), 64, 16]) mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_multihead(mlp_reg, X_train_feat, y_train, X_valid_feat, y_valid, X_test_feat, y_test, lr=args.lr, @@ -449,8 +316,8 @@ def mlp_train(args) -> None: w_cls=0.5, num_classes=args.num_classes) ###################### Save Model #################### - filename = f"{args.plots_out_path}/mlp_model_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}.pkl" - with open(filename, 'wb') as file: + run_file_name=f"{args.plots_out_path}/dqr_{args.month}_{args.target}_{args.library}_{args.embed_type}_{args.emb_model}_{'GNN-RNI' if args.use_gnn_emb else ''}_{'topic-emb' if args.use_topic_emb else ''}_run{str(i)}" + with open(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_credibench_MLP_Model.pkl", 'wb') as file: pickle.dump(mlp_reg, file) ################## Weak Supervision ########### if args.generate_weaksupervision_scores == True: @@ -467,34 +334,26 @@ def mlp_train(args) -> None: phishtank_pred = mlp_reg.predict(phishtank_features) pd.DataFrame(zip(embd_dict_phishtank.keys(), phishtank_pred), columns=["domain", f"pred_{args.target}"]).to_csv( - f"{args.plots_out_path}/Phishtank_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}_{'_+GNN-RNI' if args.use_gnn_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", - index=None) + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_Phishtank_pred.csv",index=None) # print("phishtank_pred=",phishtank_pred) URLhaus_features = [v for k, v in embd_dict_URLhaus.items()] URLhaus_pred = mlp_reg.predict(URLhaus_features) pd.DataFrame(zip(embd_dict_URLhaus.keys(), URLhaus_pred), columns=["domain", f"pred_{args.target}"]).to_csv( - f"{args.plots_out_path}/URLhaus_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}_{'_+GNN-RNI' if args.use_gnn_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", - index=None) + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_URLhaus_pred.csv",index=None) PhishDataset_legit_features = [v for k, v in embd_dict_PhishDataset_legit.items()] PhishDataset_legit_pred = mlp_reg.predict(PhishDataset_legit_features) pd.DataFrame(zip(embd_dict_PhishDataset_legit.keys(), PhishDataset_legit_pred), columns=["domain", f"pred_{args.target}"]).to_csv( - f"{args.plots_out_path}/PhishDataset_legit_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}_{'_+GNN-RNI' if args.use_gnn_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", - index=None) + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_legit_pred.csv",index=None) ################## Plot and Eval ############### true = y_test - pred = mlp_reg.predict(X_test_feat) - plot_loss(train_loss, valid_loss, test_loss, mean_loss, args.plots_out_path, target=args.target, - emb_model=args.emb_model, use_gnn_emb=args.use_gnn_emb, use_topic_emb=args.use_topic_emb, - embed_type=args.embed_type, run=i, month=args.month) - plot_histogram(true, pred, args.plots_out_path, target=args.target, emb_model=args.emb_model, - use_gnn_emb=args.use_gnn_emb, use_topic_emb=args.use_topic_emb, embed_type=args.embed_type, - run=i, month=args.month) - plot_regression_scatter(true, pred, args.plots_out_path, target=args.target, emb_model=args.emb_model, - use_gnn_emb=args.use_gnn_emb, use_topic_emb=args.use_topic_emb, - embed_type=args.embed_type, run=i, month=args.month) + pred = mlp_reg.predict(X_test_feat) + + plot_loss(train_loss, valid_loss, test_loss, mean_loss, run_file_name+"_loss.pdf") + plot_histogram(true, pred, run_file_name+"_testset_true_vs_pred_frequancy.pdf") + plot_regression_scatter(true, pred, run_file_name+"_testset_true_vs_pred_scatter.pdf") MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(pred, true) min_error_dict["domain"] = X_test.iloc[min_error_dict["test_idx"]]["domain"] max_error_dict["domain"] = X_test.iloc[max_error_dict["test_idx"]]["domain"] @@ -503,14 +362,13 @@ def mlp_train(args) -> None: f"Run{i}:MSE={MSE}\tR2={R2}\tMAE={MAE}\tMean_MAE={Mean_MAE}\tmin_error_dict={min_error_dict}\tmax_error_dict={max_error_dict}") ############ save test results ############ X_test.to_csv( - f"{args.plots_out_path}/dqr_testset_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv") + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_testset.csv",index=None) pd.DataFrame(zip(true, pred), columns=["true", "pred"]).to_csv( - f"{args.plots_out_path}/dqr_pred_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}.csv", - index=None) + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_pred.csv",index=None) results_df = pd.DataFrame(results, columns=['MSE', 'MAE', 'R2', 'Mean_MAE', 'Min_AE', 'Max_AE', 'args']) - results_df.to_csv( - f"{args.plots_out_path}/dqr_{args.month}_{args.target}_{args.embed_type}_{args.emb_model}{'_GNN-RNI' if args.use_gnn_emb else ''}{'_+IPTC-emb' if args.use_topic_emb else ''}{'_agg' if args.agg_month_emb else ''}_result.csv", ) + results_df.to_csv(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_results.csv",index=None) + for col in ['MSE', 'MAE', 'R2', 'Mean_MAE']: print(f"{col}: Mean={results_df[col].mean()}\tstd={results_df[col].std()}") @@ -531,10 +389,8 @@ def mlp_train(args) -> None: parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") parser.add_argument("--emb_dim", type=int, default=4096,help="embedding size") parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") - # parser.add_argument("--lr", type=float, default=3e-5) parser.add_argument("--lr", type=float, default=5e-3,help="learning rate") - # parser.add_argument("--lr", type=float, default=5e-4) - parser.add_argument("--epochs", type=int,default=8,help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L + parser.add_argument("--epochs", type=int,default=100, help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") parser.add_argument("--runs", type=int, default=1,help="# training runs") parser.add_argument("--use_gnn_emb", type=bool, default=True,help="append GNN embedding") @@ -545,7 +401,8 @@ def mlp_train(args) -> None: parser.add_argument("--MultiHead", type=bool, default=False, help="Use MultiHead Model") parser.add_argument("--num_classes", type=int, default=3,help="# classifcation classes for Multihead model") parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") - parser.add_argument("--month", type=str, default="nov", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--library", type=str, default="sklearn", choices=["pytorch", "sklearn"],help="ML library to use") args = parser.parse_args() print("args=", args) mlp_train(args) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/utils.py b/tgrag/experiments/mlp_experiments/utils.py new file mode 100644 index 00000000..c9fbe6ec --- /dev/null +++ b/tgrag/experiments/mlp_experiments/utils.py @@ -0,0 +1,141 @@ +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from sklearn.preprocessing import normalize +from tqdm import tqdm +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +from statistics import mean +from sklearn.model_selection import KFold +def normalize_embeddings(emb_dict): + norm_arr=normalize(list(emb_dict.values())) + return {k: norm_arr[idx] for idx,k in enumerate(emb_dict.keys())} + +def kfold_split(X, y, n_splits=5): + kf = KFold(n_splits = n_splits, shuffle = True, random_state = 42) + splits = [] + for train_index, test_index in kf.split(X): + X_tr_va, X_test = X.iloc[train_index], X.iloc[test_index] + y_tr_va, y_test = y[train_index], y[test_index] + X_train, X_val, y_train, y_val = train_test_split(X_tr_va, y_tr_va, test_size=0.25) + splits.append((X_train, y_train,X_val, y_val, X_test, y_test)) + return splits +def train_valid_test_split(target, labeled_11k_df, test_valid_size=0.4): + if target == "mbfc_bias": + quantiles = labeled_11k_df[target].quantile([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) + else: + quantiles = labeled_11k_df[target].quantile([0.2, 0.4, 0.6, 0.8, 1.0]) + bins = [labeled_11k_df[target].min()] + quantiles.tolist() + labeled_11k_df[target + '_cat'] = pd.cut(labeled_11k_df[target], bins=bins, labels=quantiles, include_lowest=True) + X = labeled_11k_df[['domain', target]] + y = labeled_11k_df[target + '_cat'] + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_valid_size, stratify=y, random_state=42 + ) + X_valid, X_test, y_valid, y_test = train_test_split( + X_test, y_test, test_size=0.5, stratify=y_test, random_state=42) + return X_train, labeled_11k_df.iloc[X_train.index][target].tolist(), X_valid, labeled_11k_df.iloc[X_valid.index][ + target].tolist(), X_test, labeled_11k_df.iloc[X_test.index][target].tolist() + + +def resize_emb(text_emb, target, X_train, X_valid, X_test, gnn_emb=None, topic_emb=None, trim_to=1024): + X_train_feat = [text_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_valid_feat = [text_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_test_feat = [text_emb[d][0:trim_to] for d in X_test["domain"].tolist()] + print("emb-size=", len(X_test_feat[0])) + if gnn_emb is not None: + X_train_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_train_feat_gnn, X_train_feat)] + + X_valid_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_valid_feat_gnn, X_valid_feat)] + + X_test_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_test["domain"].tolist()] + X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_gnn, X_test_feat)] + print("emb-size=", len(X_test_feat[0])) + if topic_emb is not None: + X_train_feat_topic = [topic_emb[d] for d in X_train["domain"].tolist()] + X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in + zip(X_train_feat_topic, X_train_feat)] + + X_valid_feat_topic = [topic_emb[d] for d in X_valid["domain"].tolist()] + X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in + zip(X_valid_feat_topic, X_valid_feat)] + + X_test_feat_topic = [topic_emb[d] for d in X_test["domain"].tolist()] + X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_topic, X_test_feat)] + print("emb-size=", len(X_test_feat[0])) + + return X_train_feat, X_valid_feat, X_test_feat + + +def plot_loss(train_loss, valid_loss, test_loss, mean_loss, out_file_path="loss_plot.pdf"): + plt.figure(figsize=(5, 4)) + plt.rc('font', size=16) + plt.plot(range(len(train_loss)), train_loss, label="train loss") + plt.plot(range(len(train_loss)), valid_loss, label="validation loss") + plt.plot(range(len(train_loss)), test_loss, label="test loss") + plt.plot(range(len(train_loss)), mean_loss, label="mean loss") + plt.xticks(range(0, len(train_loss) + 1, 1 if len(train_loss) <= 10 else len(train_loss) // 10)) + plt.xlabel("Epoch") + plt.ylabel("MSE") + plt.grid(True) + plt.legend(fontsize=14) + plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) + plt.show() + + +def plot_histogram(true, pred, out_file_path="_testset_true_vs_pred_frequancy.pdf",target="pc1"): + plt.figure(figsize=(5, 4)) + plt.hist(pred, bins=50, range=(0, 1), edgecolor='black', color='lightblue', label="Pred") + plt.hist(true, bins=50, range=(0, 1), edgecolor='black', color='orange', alpha=0.6, label="True") + y_max = max(np.histogram(true, bins=50, range=(0, 1))[0].max(), + np.histogram(pred, bins=50, range=(0, 1))[0].max()) + plt.rc('font', size=15) + plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') + plt.yticks(np.arange(0, y_max + 50, 100), rotation=0, ha='right') + plt.xlabel(target.upper().replace("_", "-")) + plt.ylabel('Frequancy') + plt.legend() + plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) + plt.show() + + +def plot_regression_scatter(true, pred, out_file_path="_testset_true_vs_pred_scatter.pdf", target="pc1",): + plt.figure(figsize=(5, 4)) + plt.rc('font', size=16) + plt.scatter(true, pred, alpha=0.7) + plt.plot([0, 1], [0, 1], color='red', linestyle='-', label='regression line') + plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') + plt.yticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') + plt.xlabel(target.upper().replace("_", "-")) + plt.ylabel(f"Predicted {target.upper().replace('_', '-')}") + plt.legend() + plt.grid(True) + plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) + plt.show() + + +def eval(pred, true): + max(abs(x - y) for x, y in zip(true, pred)) + res_df = pd.DataFrame(zip(true, pred), columns=['true', 'pred']) + res_df["diff"] = res_df.apply(lambda row: abs(row['true'] - row['pred']), axis=1) + max_idx = res_df.idxmax()["diff"] + min_idx = res_df.idxmin()["diff"] + max_diff_row = res_df.iloc[max_idx] + min_diff_row = res_df.iloc[min_idx] + max_diff_dict = max_diff_row.to_dict() + max_diff_dict["test_idx"] = max_idx + min_diff_dict = min_diff_row.to_dict() + min_diff_dict["test_idx"] = min_idx + + mse = mean_squared_error(true, pred) + # print(f"mse={mse}") + r2 = r2_score(true, pred) + # print(f"r2={r2}") + mae = mean_absolute_error(true, pred) + true_mean = mean(true) + mean_mae = mean_absolute_error(true, [true_mean for elem in true]) + # print(f"MAE={mae}") + return mse, mae, r2, mean_mae, min_diff_dict, max_diff_dict diff --git a/tgrag/webScraper/scrape_home_pages.py b/tgrag/webScraper/scrape_home_pages.py index f75e9d6c..7cf4dd64 100644 --- a/tgrag/webScraper/scrape_home_pages.py +++ b/tgrag/webScraper/scrape_home_pages.py @@ -67,7 +67,7 @@ def scrape(url): 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("--domains_path", type=str, default="../../data/weaksupervision/labels.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() @@ -86,10 +86,10 @@ def scrape(url): 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 i in tqdm(range(0, len(threads), 100)): + for thread in threads[i:i + 100]: thread.start() - for thread in threads[i:i + 1000]: + for thread in threads[i:i + 100]: 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') diff --git a/uv.lock b/uv.lock index 8d3dc7e8..343f9e53 100644 --- a/uv.lock +++ b/uv.lock @@ -677,6 +677,42 @@ wheels = [ { url = 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valid_loss=0.6872321367263794 test_loss=0.6872155070304871 +Epoch=19 Batch=0 train_loss=0.6867662668228149 valid_loss=0.6867660880088806 test_loss=0.686748743057251 +Epoch=19 Batch=80845 train_loss=0.6867310404777527 valid_loss=0.6867660880088806 test_loss=0.686748743057251 +Epoch=19 Batch=161690 train_loss=0.6869905591011047 valid_loss=0.6867660880088806 test_loss=0.686748743057251 +Epoch=19 Batch=242535 train_loss=0.6867077350616455 valid_loss=0.6867660880088806 test_loss=0.686748743057251 +Epoch=19 Batch=323380 train_loss=0.6866328120231628 valid_loss=0.6867660880088806 test_loss=0.686748743057251 +Epoch=19 Batch=404225 train_loss=0.7330095171928406 valid_loss=0.6867660880088806 test_loss=0.686748743057251 +Accuracy: 0.4635 +cm: [[ 0 72295] + [ 0 62448]] +F1 score: 0.2935 +Run0:MSE=0.5365399122238159 R2=-1.1576833724975586 MAE=0.5365399122238159 Mean_MAE=0.4634600684265602 min_error_dict={'true': 1.0, 'pred': 1.0, 'diff': 0.0, 'test_idx': np.int64(2), 'domain': 'acg-architects.com'} max_error_dict={'true': 0.0, 'pred': 1.0, 'diff': 1.0, 'test_idx': np.int64(0), 'domain': 'heeddigital.com'} +MSE: Mean=0.5365399122238159 std=nan +MAE: Mean=0.5365399122238159 std=nan +R2: Mean=-1.1576833724975586 std=nan +Mean_MAE: Mean=0.4634600684265602 std=nan diff --git a/tgrag/experiments/mlp_experiments/mlp_inference.py b/tgrag/experiments/mlp_experiments/mlp_inference.py index 6e921783..0c33e1d8 100644 --- a/tgrag/experiments/mlp_experiments/mlp_inference.py +++ b/tgrag/experiments/mlp_experiments/mlp_inference.py @@ -20,6 +20,7 @@ from multiprocessing import Pool, cpu_count import threading +from utils import search_parquet_emb as search_parquet_gnn, search_parquet_content, load_emb_index # shard_domains_dict={} # def process_chunk(chunk): # global shard_domains_dict @@ -28,21 +29,6 @@ text_emb_shards_dict={} domain_index_text_dict, domain_index_set={},() domain_index_gnn_dict, index_domain_gnn_dict={},{} -def load_emb_index(path="../../../data/Dec2024/gnn_random_v0", index_pickle="domain_shard_index.pkl",invert=True ): - with open(f'{path}/{index_pickle}', 'rb') as f: - index_dict = pickle.load(f) - # global shard_domains_dict - shard_domains_dict=None - if invert: - shard_domains_dict={} - val_set=set(index_dict.values()) - for v in val_set: - shard_domains_dict[v]=[] - - for k,v in index_dict.items(): - shard_domains_dict[v].append(k) - return index_dict,shard_domains_dict - def load_emb_dict(path="../../../data/Dec2024/gnn_random_v0", emb_pickle="domain_shard_index.pkl"): shared_key=emb_pickle.split('.')[0] if shared_key not in text_emb_shards_dict: @@ -53,25 +39,6 @@ def load_emb_dict(path="../../../data/Dec2024/gnn_random_v0", emb_pickle="domain text_emb_shards_dict[shared_key]=embd_dict return text_emb_shards_dict[shared_key] -def search_parquet_gnn(path="../../../data/Dec2024/gnn_random_v0", parquet_name="shard_0.parquet", column_name="",search_values=[]): - column_name = 'domain' - filters = [(column_name, 'in', search_values)] - table = pq.read_table(f'{path}/{parquet_name}', filters=filters) - emb_dict={} - for batch in table.to_batches(): - for i in range(len(batch)): - emb_dict[str(batch['domain'][i])]=batch['emb'][i].as_py() - return emb_dict -def search_parquet_content(path="../../../data/Dec2024/gnn_random_v0", parquet_name="shard_0.parquet", column_name="",search_values=[]): - column_name = 'domain' - filters = [(column_name, 'in', search_values)] - table = pq.read_table(f'{path}/{parquet_name}', filters=filters) - emb_dict={} - for batch in table.to_batches(): - for i in range(len(batch)): - emb_dict[str(batch['domain'][i])]=batch['embeddings'][i][0]['emb'].as_py() ## first doc emb - return emb_dict - def process_inference_batch(idx,mlp_reg,batch_domains,gnn_emb_path,text_emb_path,gnn_emb_idx): # batch_domains = gnn_v[i:i + batch_size] print(f"started: gnn_emb_idx={gnn_emb_idx}\t batch_idx={idx}") diff --git a/tgrag/experiments/mlp_experiments/mlp_modules.py b/tgrag/experiments/mlp_experiments/mlp_modules.py index 1902f1dc..0d6daf7c 100644 --- a/tgrag/experiments/mlp_experiments/mlp_modules.py +++ b/tgrag/experiments/mlp_experiments/mlp_modules.py @@ -10,7 +10,10 @@ from tqdm import tqdm import random import copy - +from sklearn.metrics import log_loss +import torch.nn.functional as F +from torch import Tensor, nn +from samplers import BalancedBatchSampler, BinaryClassificationDataset # ---------------------------- seed = 42 torch.manual_seed(seed) @@ -18,7 +21,7 @@ def train_scikitlearn_regressor(mlp_reg, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, epochs=15): batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] - early_stopper = Sklearn_EarlyStopping(patience=10) + early_stopper = Sklearn_EarlyStopping(patience=5) for _ in tqdm(range(epochs)): for b in range(0, len(Y_train), batch_size): X_batch, y_batch = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] @@ -33,9 +36,28 @@ def train_scikitlearn_regressor(mlp_reg, X_train_feat, Y_train, X_valid_feat, Y_ mlp_reg = early_stopper.restore_best_weights() break return mlp_reg, train_loss, valid_loss, test_loss, mean_loss +def train_scikitlearn_classifier(mlp_clf, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, epochs=15): + batch_size, train_loss, valid_loss, test_loss = 5000, [], [], [] + early_stopper = Sklearn_EarlyStopping(patience=5) + all_classes = np.unique(Y_train) + for i in tqdm(range(epochs)): + for b in range(0, len(Y_train), batch_size): + X_batch, y_batch = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] + if i == 0: + mlp_clf.partial_fit(X_batch, y_batch, classes=all_classes) + else: + mlp_clf.partial_fit(X_batch, y_batch) + train_loss.append(mlp_clf.loss_) + valid_loss.append(log_loss(Y_valid, mlp_clf.predict_proba(X_valid_feat))) + test_loss.append(log_loss(Y_test, mlp_clf.predict_proba(X_test_feat))) + if early_stopper.step(valid_loss[-1], mlp_clf): + print("Early stopping triggered.") + mlp_clf = early_stopper.restore_best_weights() + break + return mlp_clf, train_loss, valid_loss, test_loss,None class EarlyStopping: - def __init__(self, patience=10, min_delta=0.0): + def __init__(self, patience=5, min_delta=0.0): self.patience = patience self.min_delta = min_delta self.best_loss = float("inf") @@ -55,7 +77,7 @@ def step(self, val_loss, model): def restore_best_weights(self, model): model.load_state_dict(self.best_state) class Sklearn_EarlyStopping: - def __init__(self, patience=10, min_delta=0.0): + def __init__(self, patience=5, min_delta=0.0): self.patience = patience self.min_delta = min_delta self.best_loss = float("inf") @@ -98,6 +120,22 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x def predict(self, x): return self.forward(torch.tensor(x).float()).detach().squeeze(1).numpy() +class LabelPredictor(nn.Module): + def __init__( + self, in_dim: int, hidden_dim_multiplier: float = 0.5, out_dim: int = 2 + ): + super().__init__() + hidden_dim = int(hidden_dim_multiplier * in_dim) + self.lin_node = nn.Linear(in_dim, hidden_dim) + self.out = nn.Linear(hidden_dim, out_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.lin_node(x) + x = x.relu() + x = self.out(x) + return torch.log_softmax(x, dim=-1) + def predict(self, x): + return self.forward(torch.tensor(x).float()).argmax(dim=-1) class MLPRegressor(nn.Module): def __init__(self, input_size, hidden_layer_sizes): @@ -118,7 +156,8 @@ def forward(self, x): def predict(self, x): return self.forward(torch.tensor(x).float()).detach().squeeze(1).numpy() def train_mlp(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15): - batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] + model.train() + batch_size, train_loss, valid_loss, test_loss, mean_loss = len(Y_train)//5, [], [], [], [] optimizer = torch.optim.Adam(model.parameters(), lr=lr) criterion_reg = nn.MSELoss() early_stopper = EarlyStopping(patience=10) @@ -146,6 +185,79 @@ def train_mlp(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, break return model, train_loss, valid_loss, test_loss, mean_loss +def train_classifier(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15): + model.train() + batch_size, train_loss_lst, valid_loss_lst, test_loss_lst = len(Y_train)//5, [], [], [] + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion_clf =F.nll_loss + early_stopper = EarlyStopping(patience=5) + for epoch in tqdm(range(epochs)): + for b in range(0, len(Y_train), batch_size): + optimizer.zero_grad() + X_batch, y_reg = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] + batch_mean = sum(y_reg) / len(y_reg) + clf_out= model(torch.tensor(X_batch).float()) + train_loss= criterion_clf(clf_out.float(), torch.tensor(y_reg)) + if epochs>(epoch+1): + train_loss.backward() + optimizer.step() + + with torch.no_grad(): + train_loss_lst.append(train_loss.detach().numpy()) + val_clf_out = model(torch.tensor(X_valid_feat).float()) + # val_clf_out=val_clf_out.argmax(dim=-1) + valid_loss_lst.append(criterion_clf(val_clf_out.detach().float().squeeze(), torch.tensor(Y_valid).squeeze()).detach().numpy()) + test_clf_out = model(torch.tensor(X_test_feat).float()) + # test_clf_out=test_clf_out.argmax(dim=-1) + test_loss_lst.append(criterion_clf(test_clf_out.detach().float().squeeze(), torch.tensor(Y_test).squeeze()).detach().numpy()) + print(f"Epoch={epoch}\t Batch={b}\t train_loss={train_loss_lst[-1]}\t valid_loss={valid_loss_lst[-1]}\t test_loss={test_loss_lst[-1]}") + + if early_stopper.step(valid_loss_lst[-1], model): + print("Early stopping triggered.") + early_stopper.restore_best_weights(model) + break + return model, train_loss_lst, valid_loss_lst, test_loss_lst, None + + + +def train_classifier_unbalanced(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15,batch_size=8192): + sampler = BalancedBatchSampler(Y_train, batch_size=batch_size) + dataset = BinaryClassificationDataset(X_train_feat, Y_train) + + # for batch_idx, (X_batch, y_reg) in enumerate(train_loader): + # print(f"Batch {batch_idx}: X_batch shape: {X_batch.shape}, y_reg shape: {y_reg.shape}") + + model.train() + batch_size, train_loss_lst, valid_loss_lst, test_loss_lst = len(Y_train)//5, [], [], [] + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion_clf =F.nll_loss + early_stopper = EarlyStopping(patience=5) + for epoch in tqdm(range(epochs)): + train_loader = DataLoader(dataset, batch_sampler=sampler) + for batch_idx, (X_batch, y_reg) in enumerate(train_loader): + optimizer.zero_grad() + clf_out= model(torch.tensor(X_batch).float()) + train_loss= criterion_clf(clf_out.float(), torch.tensor(y_reg).long()) + if epochs>(epoch+1): + train_loss.backward() + optimizer.step() + + with torch.no_grad(): + train_loss_lst.append(train_loss.detach().numpy()) + val_clf_out = model(torch.tensor(X_valid_feat).float()) + # val_clf_out=val_clf_out.argmax(dim=-1) + valid_loss_lst.append(criterion_clf(val_clf_out.detach().float().squeeze(), torch.tensor(Y_valid).squeeze()).detach().numpy()) + test_clf_out = model(torch.tensor(X_test_feat).float()) + # test_clf_out=test_clf_out.argmax(dim=-1) + test_loss_lst.append(criterion_clf(test_clf_out.detach().float().squeeze(), torch.tensor(Y_test).squeeze()).detach().numpy()) + print(f"Epoch={epoch}\t Batch={batch_idx}\t train_loss={train_loss_lst[-1]}\t valid_loss={valid_loss_lst[-1]}\t test_loss={test_loss_lst[-1]}") + + if early_stopper.step(valid_loss_lst[-1], model): + print("Early stopping triggered.") + early_stopper.restore_best_weights(model) + break + return model, train_loss_lst, valid_loss_lst, test_loss_lst, None + class MultiTaskMLP(nn.Module): def __init__(self, input_dim, num_classes, hidden_dims=[128, 64]): diff --git a/tgrag/experiments/mlp_experiments/mlp_train.py b/tgrag/experiments/mlp_experiments/mlp_train.py index 73687466..a9480f13 100644 --- a/tgrag/experiments/mlp_experiments/mlp_train.py +++ b/tgrag/experiments/mlp_experiments/mlp_train.py @@ -250,7 +250,7 @@ def mlp_train(args) -> None: ################# Train ##################### results = [] for i in range(args.runs): - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, labeled_11k_df,args.test_valid_size) + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, labeled_11k_df,key='domain',test_valid_size=args.test_valid_size) if args.embed_type == "TFIDF": print(f"TFIDF dim={text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]}") X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, X_test, @@ -265,7 +265,7 @@ def mlp_train(args) -> None: gnn_emb_dict = load_emb_dict("GNN_GAT", args.gnn_emb_path,month=args.month) labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(gnn_emb_dict.keys())] labeled_11k_df = labeled_11k_df.reset_index(drop=True) - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target,labeled_11k_df,args.test_valid_size) + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target,labeled_11k_df,key='domain',test_valid_size=args.test_valid_size) else: gnn_emb_dict = None if args.use_topic_emb: diff --git a/tgrag/experiments/mlp_experiments/mlp_train_classifier.py b/tgrag/experiments/mlp_experiments/mlp_train_classifier.py new file mode 100644 index 00000000..24d451c1 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/mlp_train_classifier.py @@ -0,0 +1,315 @@ +import argparse +import sklearn +import logging +from typing import Dict, cast +import pickle +import numpy as np +from tgrag.utils.args import parse_args +from tgrag.utils.logger import setup_logging +from tgrag.utils.path import get_root_dir +from tqdm import tqdm +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.neural_network import MLPClassifier as Sklearn_MLPClassifier +from mlp_modules import MLPRegressor +from mlp_modules import MLP3LayersPredictor +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from statistics import mean +from mlp_modules import MultiTaskMLP, train_multihead,train_mlp,train_scikitlearn_classifier,train_classifier,LabelPredictor,train_classifier_unbalanced +from sklearn.preprocessing import normalize +from utils import normalize_embeddings, plot_histogram,train_valid_test_split,resize_emb,\ + plot_histogram,plot_loss,plot_regression_scatter,eval,\ + plot_classesCount +from sklearn.metrics import confusion_matrix, accuracy_score +from sklearn.metrics import f1_score +from utils import search_parquet_emb,search_parquet_duckdb +def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg",normalize=False): + months_emb_lst = [] + if embed_type == "GNN_GAT": + for month in month_lst: + with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + if normalize: + embd_dict=normalize_embeddings(embd_dict) + months_emb_lst.append(embd_dict) + common_domains_set = set(months_emb_lst[0].keys()).intersection(months_emb_lst[1].keys()).intersection( + months_emb_lst[2].keys()) + + for key in common_domains_set: + for i in range(1, len(months_emb_lst)): + if agg == "concat": + months_emb_lst[0][key].extend(months_emb_lst[i][key]) + elif agg == "min": + months_emb_lst[0][key] = [min(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] + elif agg == "max": + months_emb_lst[0][key] = [max(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] + elif agg == "avg": + months_emb_lst[0][key] = [(a + b) / 2 for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] + + concat_dict = {k: v for k, v in months_emb_lst[0].items() if k in common_domains_set} + # print(f"concat Nmonth emb size={len(concat_dict[list(concat_dict.keys())[0]])}") + # print(f"len of keys={len(concat_dict.keys())}") + return concat_dict + + +def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): + months_emb_PhishTank_lst = [] + months_emb_URLhaus_lst = [] + months_emb_legit_lst = [] + for month in month_lst: + with open(f'{path}/PhishTank_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_PhishTank_lst.append(pickle.load(f)) + with open(f'{path}/URLHaus_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_URLhaus_lst.append(pickle.load(f)) + with open(f'{path}/IP2Location_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_legit_lst.append(pickle.load(f)) + + for ds_months in [months_emb_PhishTank_lst, months_emb_URLhaus_lst, months_emb_legit_lst]: + for key in ds_months[0].keys(): + for i in range(1, len(ds_months)): # loop on dataset months + if key in ds_months[i]: + if agg == "concat": + ds_months[0][key].extend(ds_months[i][key]) + # print(len(ds_months[0][key])) + elif agg == "min": + ds_months[0][key] = [min(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "max": + ds_months[0][key] = [max(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "avg": + ds_months[0][key] = [(a + b) / 2 for a, b in zip(ds_months[0][key], ds_months[i][key])] + # print(len(ds_months[0][key])) + return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] + + +def load_emb_dict(embed_type, path="../../../data",pickle_name=None, model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=8192,normalize=False): + if pickle_name: + with open(f'{path}/{pickle_name}', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "domainName": + with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + # with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'][0:emb_dim] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1][0:emb_dim] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) + return embd_dict + +def load_emb_dict_from_parquet(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=8192,normalize=False): + embd_dict=None + if embed_type == "text": + if model_name == "embeddinggemma-300m": + # embd_dict=search_parquet_emb(path,f'weaksupervision_content_emb_{month}2024.parquet', column_name="domain",search_values=None,schema={'key':'domain','val':'embeddings'}) + embd_dict=search_parquet_duckdb(f'{path}/weaksupervision_content_emb_{month}2024.parquet', col="domain",q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) + elif embed_type == "GNN_GAT": + with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'][0:emb_dim] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1][0:emb_dim] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) + return embd_dict + + + +def mlp_classifier(args) -> None: + ############## Load training data and split ############### + full_emb_dict = load_emb_dict(args.embed_type, args.text_emb_path, pickle_name="weak_content_emb_embeddinggemma-300m_768.pkl",emb_dim=256,normalize=False) + month_emb_dict = load_emb_dict_from_parquet(args.embed_type, args.text_emb_path, args.emb_model, args.month,normalize=False) + weaklabeles_df = pd.read_csv(f"{args.weaksupervision_path}/weaklabels.csv") + # targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") + # targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(full_emb_dict)] + weaklabeles_df = weaklabeles_df.reset_index(drop=True) + text_emb_dict={} + text_emb_dict.update(month_emb_dict) + text_emb_dict.update({k:v for k,v in full_emb_dict.items() if k not in month_emb_dict}) + ############### filter by the GNN graph node splits ################### + if args.filter_by_GNN_nodes: + test_domains_df=search_parquet_duckdb(f'{args.weaksupervision_path}/splits/{args.month}2024/test_split.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + test_domains_df['domain']=test_domains_df['Domains_test'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + print(f"test set lables count ={weaklabeles_df[weaklabeles_df["domain"].isin(test_domains_df['domain'])]["weak_label"].value_counts()}") + valid_domains_df=search_parquet_duckdb(f'{args.weaksupervision_path}/splits/{args.month}2024/valid_split.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + valid_domains_df['domain']=valid_domains_df['Domains_valid'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + print(f"valid set lables count ={weaklabeles_df[weaklabeles_df["domain"].isin(valid_domains_df['domain'])]["weak_label"].value_counts()}") + train_domains_df=search_parquet_duckdb(f'{args.weaksupervision_path}/splits/{args.month}2024/train_split.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + train_domains_df['domain']=train_domains_df['Domains_train'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + print(f"train set lables count ={weaklabeles_df[weaklabeles_df["domain"].isin(train_domains_df['domain'])]["weak_label"].value_counts()}") + test_domains_set=set(test_domains_df['domain']) + valid_domains_set=set(valid_domains_df['domain']) + train_domains_set=set(train_domains_df['domain']) + filter_by_domains_set=test_domains_set.union(valid_domains_set).union(train_domains_set) + weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(filter_by_domains_set)] + weaklabeles_df = weaklabeles_df.reset_index(drop=True) + text_emb_dict={k:v for k,v in text_emb_dict.items() if k in filter_by_domains_set} + ################# Train ##################### + results = [] + for i in range(args.runs): + if args.filter_by_GNN_nodes: + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, weaklabeles_df,key='domain',test_valid_size=args.test_valid_size,regressor=False,train_lst=train_domains_set,valid_lst=valid_domains_set,test_lst=test_domains_set) + else: + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, weaklabeles_df,key='domain',test_valid_size=args.test_valid_size,regressor=False) + ############### Resize embeddings ################## + if args.embed_type == "TFIDF": + print(f"TFIDF dim={text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]}") + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, X_test, + gnn_emb=None, trim_to= + text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]) + else: + if args.use_gnn_emb or args.use_topic_emb: + if args.use_gnn_emb: + if args.agg_month_emb: + gnn_emb_dict = load_agg_Nmonth_emb_dict("GNN_GAT", args.emb_path, agg="avg") + else: + gnn_emb_dict = load_emb_dict("GNN_GAT", args.gnn_emb_path,month=args.month) + weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(gnn_emb_dict.keys())] + weaklabeles_df = weaklabeles_df.reset_index(drop=True) + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target,weaklabeles_df,args.test_valid_size,regressor=False) + else: + gnn_emb_dict = None + if args.use_topic_emb: + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_freq", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_emb", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("3Feat", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("3Feat2", args.emb_path) + # gnn_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) + topic_iptc_emb_dict = load_emb_dict("PASTEL_hasContent", args.text_emb_path) + else: + topic_iptc_emb_dict = None + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, + X_test, gnn_emb=gnn_emb_dict, + topic_emb=topic_iptc_emb_dict, + trim_to=args.emb_dim) + else: + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, + X_test, gnn_emb=None, topic_emb=None, + trim_to=args.emb_dim) + + print(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") + if args.MultiHead == False: + ###################### PYTorch Regressor ###################### + if args.library=="pytorch": + mlp_clf = LabelPredictor(len(X_train_feat[0])) + # mlp_clf = MLPRegressor(len(X_train_feat[0]),hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16)) + mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_classifier_unbalanced(mlp_clf, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + + # mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_classifier(mlp_clf, X_train_feat, y_train, + # X_valid_feat, y_valid, X_test_feat, + # y_test, epochs=args.epochs) + ######################## Scikit-Learn ################### + elif args.library=="sklearn": + mlp_clf = Sklearn_MLPClassifier(hidden_layer_sizes=(128, 32), + activation='relu', solver='adam',max_iter=args.max_iter, random_state=42, + verbose=False, learning_rate_init=args.lr,warm_start=True) + mlp_clf.out_activation_ = 'sigmoid' + mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_scikitlearn_classifier(mlp_clf, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + + else: + mlp_clf = MultiTaskMLP(input_dim=len(X_train_feat[0]), num_classes=args.num_classes, + hidden_dims=[int(len(X_train_feat[0]) * 0.9), 64, 16]) + mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_multihead(mlp_clf, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, lr=args.lr, + epochs=args.epochs, w_reg=0.5, + w_cls=0.5, + num_classes=args.num_classes) + ###################### Save Model #################### + run_file_name=f"{args.plots_out_path}/weaksupervision_{args.month}_{args.target}_{args.library}_{args.embed_type}_{args.emb_model}_{'GNN-RNI' if args.use_gnn_emb else ''}_{'topic-emb' if args.use_topic_emb else ''}_run{str(i)}" + with open(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_credibench_MLP_Model.pkl", 'wb') as file: + pickle.dump(mlp_clf, file) + ################## Plot and Eval ############### + true = y_test + pred = mlp_clf.predict(X_test_feat) + pred=pred.round() + accuracy = accuracy_score(true, pred) + print(f"Accuracy: {accuracy:.4f}") + cm = confusion_matrix(true, pred) + print(f"cm: {cm}") + f1 = f1_score(true,pred, average='macro') + print(f"F1 score (macro): {f1:.4f}") + + plot_loss(train_loss, valid_loss, test_loss, mean_loss, run_file_name+"_loss.pdf",ylabel="CrossEntropy Loss") + plot_classesCount(cm,run_file_name+"_class_frequancy.pdf") + plot_regression_scatter(true, pred, run_file_name+"_testset_true_vs_pred_scatter.pdf") + MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(pred, true) + min_error_dict["domain"] = X_test.iloc[min_error_dict["test_idx"]]["domain"] + max_error_dict["domain"] = X_test.iloc[max_error_dict["test_idx"]]["domain"] + # results.append([MSE, MAE, R2, Mean_MAE, str(min_error_dict), str(max_error_dict), str(args)]) + # print(f"Run{i}:MSE={MSE}\tR2={R2}\tMAE={MAE}\tMean_MAE={Mean_MAE}\tmin_error_dict={min_error_dict}\tmax_error_dict={max_error_dict}") + ############ save test results ############ + X_test.to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_testset.csv",index=None) + pd.DataFrame(zip(true, pred), columns=["true", "pred"]).to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_pred.csv",index=None) + + results_df = pd.DataFrame(results, columns=['MSE', 'MAE', 'R2', 'Mean_MAE', 'Min_AE', 'Max_AE', 'args']) + results_df.to_csv(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_results.csv",index=None) + + # for col in ['MSE', 'MAE', 'R2', 'Mean_MAE']: + # print(f"{col}: Mean={results_df[col].mean()}\tstd={results_df[col].std()}") + + +if __name__ == '__main__': + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP Experiments") + parser.add_argument("--target", type=str, default="weak_label", choices=["weak_label"], help="the credability target") + parser.add_argument("--text_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") + parser.add_argument("--gnn_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") + parser.add_argument("--weaksupervision_path", type=str, default=str(root + "/data/weaksupervision"),help="dqr dataset path") + parser.add_argument("--embed_type", type=str, default="text", + choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") + parser.add_argument("--emb_model", type=str, default="embeddinggemma-300m", + choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", + "IPTC_Topic_emb"],help="LLM embedding model") + parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") + parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") + parser.add_argument("--emb_dim", type=int, default=256,help="embedding size") + parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") + parser.add_argument("--lr", type=float, default=1e-2,help="learning rate") + parser.add_argument("--epochs", type=int,default=200, help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L + parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") + parser.add_argument("--runs", type=int, default=1,help="# training runs") + parser.add_argument("--use_gnn_emb", type=bool, default=False,help="append GNN embedding") + parser.add_argument("--agg_month_emb", type=bool, default=False, help="aggregate montly GNN embeddings") + parser.add_argument("--use_topic_emb", type=bool, default=False,help="use topic modeling features") + parser.add_argument("--filter_by_GNN_nodes", type=bool, default=True,help="filter by domains has GNN embeddings") + parser.add_argument("--MultiHead", type=bool, default=False, help="Use MultiHead Model") + parser.add_argument("--num_classes", type=int, default=2,help="# classifcation classes for Multihead model") + parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") + parser.add_argument("--month", type=str, default="nov", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--library", type=str, default="pytorch", choices=["pytorch", "sklearn"],help="ML library to use") + args = parser.parse_args() + print("args=", args) + mlp_classifier(args) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/samplers.py b/tgrag/experiments/mlp_experiments/samplers.py new file mode 100644 index 00000000..42fefee0 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/samplers.py @@ -0,0 +1,53 @@ +import torch +from torch.utils.data import Dataset, DataLoader, Sampler +import numpy as np + +class BinaryClassificationDataset(Dataset): + def __init__(self, X, y): + """ + X: Tensor or ndarray of shape [N, D] + y: Tensor or ndarray of shape [N] + """ + self.X = torch.as_tensor(X, dtype=torch.float32) + self.y = torch.as_tensor(y, dtype=torch.float32) + + def __len__(self): + return self.X.shape[0] + + def __getitem__(self, idx): + return self.X[idx], self.y[idx] + +class BalancedBatchSampler(Sampler): + def __init__(self, labels, batch_size): + self.labels = np.array(labels) + self.batch_size = batch_size + self.half = batch_size // 2 + + self.idx_0 = np.where(self.labels == 0)[0] + self.idx_1 = np.where(self.labels == 1)[0] + + assert len(self.idx_0) > 0 and len(self.idx_1) > 0 + + def __iter__(self): + np.random.shuffle(self.idx_0) + np.random.shuffle(self.idx_1) + + ptr0, ptr1 = 0, 0 + while ptr0 + self.half <= len(self.idx_0): + if ptr1 + self.half > len(self.idx_1): + np.random.shuffle(self.idx_1) + ptr1 = 0 + + batch = np.concatenate([ + self.idx_0[ptr0:ptr0 + self.half], + self.idx_1[ptr1:ptr1 + self.half] + ]) + + np.random.shuffle(batch) + yield batch.tolist() + + ptr0 += self.half + ptr1 += self.half + + def __len__(self): + return len(self.idx_0) // self.half diff --git a/tgrag/experiments/mlp_experiments/utils.py b/tgrag/experiments/mlp_experiments/utils.py index c9fbe6ec..19bbf634 100644 --- a/tgrag/experiments/mlp_experiments/utils.py +++ b/tgrag/experiments/mlp_experiments/utils.py @@ -8,10 +8,18 @@ import numpy as np from statistics import mean from sklearn.model_selection import KFold +from typing import Dict, cast +import pickle +import pyarrow.parquet as pq +import duckdb +import pyarrow as pa + + def normalize_embeddings(emb_dict): norm_arr=normalize(list(emb_dict.values())) return {k: norm_arr[idx] for idx,k in enumerate(emb_dict.keys())} + def kfold_split(X, y, n_splits=5): kf = KFold(n_splits = n_splits, shuffle = True, random_state = 42) splits = [] @@ -21,65 +29,80 @@ def kfold_split(X, y, n_splits=5): X_train, X_val, y_train, y_val = train_test_split(X_tr_va, y_tr_va, test_size=0.25) splits.append((X_train, y_train,X_val, y_val, X_test, y_test)) return splits -def train_valid_test_split(target, labeled_11k_df, test_valid_size=0.4): - if target == "mbfc_bias": - quantiles = labeled_11k_df[target].quantile([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) +def train_valid_test_split(target, labeled_11k_df,key='domain', test_valid_size=0.4, regressor=True,train_lst=None, valid_lst=None, test_lst=None): + X = labeled_11k_df[[key, target]] + if train_lst is not None and valid_lst is not None and test_lst is not None: + X_train = X[X[key].isin(train_lst)] + X_valid = X[X[key].isin(valid_lst)] + X_test = X[X[key].isin(test_lst)] + return X_train,X_train[target].tolist(), X_valid,X_valid[target].tolist(), X_test,X_test[target].tolist() else: - quantiles = labeled_11k_df[target].quantile([0.2, 0.4, 0.6, 0.8, 1.0]) - bins = [labeled_11k_df[target].min()] + quantiles.tolist() - labeled_11k_df[target + '_cat'] = pd.cut(labeled_11k_df[target], bins=bins, labels=quantiles, include_lowest=True) - X = labeled_11k_df[['domain', target]] - y = labeled_11k_df[target + '_cat'] - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=test_valid_size, stratify=y, random_state=42 - ) - X_valid, X_test, y_valid, y_test = train_test_split( - X_test, y_test, test_size=0.5, stratify=y_test, random_state=42) - return X_train, labeled_11k_df.iloc[X_train.index][target].tolist(), X_valid, labeled_11k_df.iloc[X_valid.index][ - target].tolist(), X_test, labeled_11k_df.iloc[X_test.index][target].tolist() - + if regressor: + if target == "mbfc_bias": + quantiles = labeled_11k_df[target].quantile([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) + else: + quantiles = labeled_11k_df[target].quantile([0.2, 0.4, 0.6, 0.8, 1.0]) + bins = [labeled_11k_df[target].min()] + quantiles.tolist() + labeled_11k_df[target + '_cat'] = pd.cut(labeled_11k_df[target], bins=bins, labels=quantiles, include_lowest=True) + y = labeled_11k_df[target + '_cat'] + else: + # bins = np.linspace(0, 0.9, 10) + # y = np.digitize(labeled_11k_df[target].tolist(), bins) + y=labeled_11k_df[target].tolist() + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_valid_size, stratify=y, random_state=42 + ) + X_valid, X_test, y_valid, y_test = train_test_split( + X_test, y_test, test_size=0.5, stratify=y_test, random_state=42) + if regressor: + return X_train, labeled_11k_df.iloc[X_train.index][target].tolist(), X_valid, labeled_11k_df.iloc[X_valid.index][ + target].tolist(), X_test, labeled_11k_df.iloc[X_test.index][target].tolist() + else: + return X_train, np.array(y_train), X_valid,np.array(y_valid) , X_test,np.array(y_test) def resize_emb(text_emb, target, X_train, X_valid, X_test, gnn_emb=None, topic_emb=None, trim_to=1024): - X_train_feat = [text_emb[d][0:trim_to] for d in X_train["domain"].tolist()] - X_valid_feat = [text_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] - X_test_feat = [text_emb[d][0:trim_to] for d in X_test["domain"].tolist()] - print("emb-size=", len(X_test_feat[0])) - if gnn_emb is not None: - X_train_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_train["domain"].tolist()] - X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_train_feat_gnn, X_train_feat)] - - X_valid_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] - X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_valid_feat_gnn, X_valid_feat)] - - X_test_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_test["domain"].tolist()] - X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_gnn, X_test_feat)] + X_train_feat = [text_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_valid_feat = [text_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_test_feat = [text_emb[d][0:trim_to] for d in X_test["domain"].tolist()] print("emb-size=", len(X_test_feat[0])) - if topic_emb is not None: - X_train_feat_topic = [topic_emb[d] for d in X_train["domain"].tolist()] - X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in - zip(X_train_feat_topic, X_train_feat)] + if gnn_emb is not None: + X_train_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_train_feat_gnn, X_train_feat)] - X_valid_feat_topic = [topic_emb[d] for d in X_valid["domain"].tolist()] - X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in - zip(X_valid_feat_topic, X_valid_feat)] + X_valid_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_valid_feat_gnn, X_valid_feat)] - X_test_feat_topic = [topic_emb[d] for d in X_test["domain"].tolist()] - X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_topic, X_test_feat)] - print("emb-size=", len(X_test_feat[0])) + X_test_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_test["domain"].tolist()] + X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_gnn, X_test_feat)] + print("emb-size=", len(X_test_feat[0])) + if topic_emb is not None: + X_train_feat_topic = [topic_emb[d] for d in X_train["domain"].tolist()] + X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in + zip(X_train_feat_topic, X_train_feat)] + + X_valid_feat_topic = [topic_emb[d] for d in X_valid["domain"].tolist()] + X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in + zip(X_valid_feat_topic, X_valid_feat)] + + X_test_feat_topic = [topic_emb[d] for d in X_test["domain"].tolist()] + X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_topic, X_test_feat)] + print("emb-size=", len(X_test_feat[0])) - return X_train_feat, X_valid_feat, X_test_feat + return X_train_feat, X_valid_feat, X_test_feat -def plot_loss(train_loss, valid_loss, test_loss, mean_loss, out_file_path="loss_plot.pdf"): +def plot_loss(train_loss, valid_loss, test_loss, mean_loss, out_file_path="loss_plot.pdf",ylabel="MSE"): plt.figure(figsize=(5, 4)) plt.rc('font', size=16) plt.plot(range(len(train_loss)), train_loss, label="train loss") plt.plot(range(len(train_loss)), valid_loss, label="validation loss") plt.plot(range(len(train_loss)), test_loss, label="test loss") - plt.plot(range(len(train_loss)), mean_loss, label="mean loss") + if mean_loss is not None and len(mean_loss)>0: + plt.plot(range(len(train_loss)), mean_loss, label="mean loss") plt.xticks(range(0, len(train_loss) + 1, 1 if len(train_loss) <= 10 else len(train_loss) // 10)) plt.xlabel("Epoch") - plt.ylabel("MSE") + plt.ylabel(ylabel) plt.grid(True) plt.legend(fontsize=14) plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) @@ -101,6 +124,24 @@ def plot_histogram(true, pred, out_file_path="_testset_true_vs_pred_frequancy.pd plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) plt.show() +def plot_classesCount(cm, out_file_path="_testset_true_vs_pred_frequancy.pdf",target="pc1"): + plt.figure(figsize=(5, 4)) + per_class_acc = np.diag(cm) / cm.sum(axis=1) + per_class_acc = np.nan_to_num(per_class_acc) + classes = np.arange(len(per_class_acc)) + plt.bar(classes, per_class_acc) + plt.xlabel("Class Label") + plt.ylabel("Accuracy") + plt.title("Per-Class Accuracy Derived from Confusion Matrix") + plt.ylim(0, 1) + plt.xticks(classes, classes) + for i, v in enumerate(per_class_acc): + plt.text(i, v + 0.02, f"{v:.2f}", ha="center", fontsize=9) + plt.tight_layout() + plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) + plt.show() + + def plot_regression_scatter(true, pred, out_file_path="_testset_true_vs_pred_scatter.pdf", target="pc1",): plt.figure(figsize=(5, 4)) @@ -139,3 +180,86 @@ def eval(pred, true): mean_mae = mean_absolute_error(true, [true_mean for elem in true]) # print(f"MAE={mae}") return mse, mae, r2, mean_mae, min_diff_dict, max_diff_dict + + +def load_emb_index(path="../../../data/Dec2024/gnn_random_v0", index_pickle="domain_shard_index.pkl",invert=True ): + with open(f'{path}/{index_pickle}', 'rb') as f: + index_dict = pickle.load(f) + # global shard_domains_dict + shard_domains_dict=None + if invert: + shard_domains_dict={} + val_set=set(index_dict.values()) + for v in val_set: + shard_domains_dict[v]=[] + + for k,v in index_dict.items(): + shard_domains_dict[v].append(k) + return index_dict,shard_domains_dict + + + +def search_parquet_emb(path="../../../data/Dec2024/gnn_random_v0", parquet_name="shard_0.parquet", column_name="",search_values=[],schema={'key':'domain','val':'emb'}): + column_name = 'domain' + if not search_values or len(search_values)==0: + table = pq.read_table(f'{path}/{parquet_name}', filters=None) + else: + filters = [(column_name, 'in', search_values)] + table = pq.read_table(f'{path}/{parquet_name}', filters=filters) + + emb_dict={} + for batch in table.to_batches(): + for i in range(len(batch)): + emb_dict[str(batch[schema['key']][i])]=batch[schema['val']][i].as_py() + return emb_dict +def search_parquet_content(path="../../../data/Dec2024/gnn_random_v0", parquet_name="shard_0.parquet", column_name="",search_values=[]): + column_name = 'domain' + filters = [(column_name, 'in', search_values)] + table = pq.read_table(f'{path}/{parquet_name}', filters=filters) + emb_dict={} + for batch in table.to_batches(): + for i in range(len(batch)): + emb_dict[str(batch['domain'][i])]=batch['embeddings'][i][0]['emb'].as_py() ## first doc emb + return emb_dict + +def search_parquet_duckdb(f_path,col,q_domains,max_memory="4GB",threads=8,batch_size=1e4,schema={'key':'domain','val':'emb'}): + con = duckdb.connect() + con.execute(f"SET memory_limit='{max_memory}'") + con.execute(f"SET threads={threads}") + if not q_domains or len(q_domains)==0: + query=f"SELECT * FROM read_parquet('{f_path}')" + result = con.execute(query) + else: + query=f"SELECT * FROM read_parquet('{f_path}') WHERE {col} IN ?" + result = con.execute(query, [list(q_domains)]) + + cols_map={name[0]:idx for idx,name in enumerate(result.description)} + if schema is None: + res=[] + while True: + rows = result.fetchmany(int(batch_size)) + if not rows: + break + for row in rows: + res.append([elem for elem in row]) + res=pd.DataFrame(res,columns=cols_map.keys()) + else: + res={} + while True: + rows = result.fetchmany(int(batch_size)) + if not rows: + break + for row in rows: + res[row[cols_map[schema['key']]]]=row[cols_map[schema['val']]] + return res + +def write_domain_emb_parquet(rows,directory_path,file_name): + schema = pa.schema([ + ("domain", pa.string()), + ("embeddings", pa.list_( pa.struct([ + ("page", pa.string()), + ("emb", pa.list_(pa.float32())) + ]) ))]) + table = pa.Table.from_pydict(rows, schema=schema) + table = table.sort_by("domain") + pq.write_table(table, f"{directory_path}/{file_name}",row_group_size=100,use_dictionary=["domain"]) \ No newline at end of file diff --git a/tgrag/webScraper/scrape_home_pages.py b/tgrag/webScraper/scrape_home_pages.py index 7cf4dd64..3c2f1858 100644 --- a/tgrag/webScraper/scrape_home_pages.py +++ b/tgrag/webScraper/scrape_home_pages.py @@ -79,7 +79,8 @@ def scrape(url): del domains_df 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)): + for b_idx in tqdm(range(392000, 400000, batch_size)): + print(f"Scraping batch starting at index: {b_idx}") threads = [] results = [] ############### MultiThreading ########### From c4bfbcae40442589fed4318bd06f673c6ea79068 Mon Sep 17 00:00:00 2001 From: hussien Date: Tue, 27 Jan 2026 12:53:55 -0500 Subject: [PATCH 7/8] random unblanced labels sampler (smaller last batch fix) --- .../mlp_experiments/mlp_modules.py | 2 +- tgrag/experiments/mlp_experiments/samplers.py | 28 ++++++++----------- 2 files changed, 13 insertions(+), 17 deletions(-) diff --git a/tgrag/experiments/mlp_experiments/mlp_modules.py b/tgrag/experiments/mlp_experiments/mlp_modules.py index 0d6daf7c..709d1b2d 100644 --- a/tgrag/experiments/mlp_experiments/mlp_modules.py +++ b/tgrag/experiments/mlp_experiments/mlp_modules.py @@ -220,7 +220,7 @@ def train_classifier(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test -def train_classifier_unbalanced(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15,batch_size=8192): +def train_classifier_unbalanced(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15,batch_size=int(1e4)): sampler = BalancedBatchSampler(Y_train, batch_size=batch_size) dataset = BinaryClassificationDataset(X_train_feat, Y_train) diff --git a/tgrag/experiments/mlp_experiments/samplers.py b/tgrag/experiments/mlp_experiments/samplers.py index 42fefee0..da8a6af3 100644 --- a/tgrag/experiments/mlp_experiments/samplers.py +++ b/tgrag/experiments/mlp_experiments/samplers.py @@ -31,23 +31,19 @@ def __init__(self, labels, batch_size): def __iter__(self): np.random.shuffle(self.idx_0) np.random.shuffle(self.idx_1) - - ptr0, ptr1 = 0, 0 - while ptr0 + self.half <= len(self.idx_0): - if ptr1 + self.half > len(self.idx_1): - np.random.shuffle(self.idx_1) - ptr1 = 0 - - batch = np.concatenate([ - self.idx_0[ptr0:ptr0 + self.half], - self.idx_1[ptr1:ptr1 + self.half] - ]) - - np.random.shuffle(batch) - yield batch.tolist() - + for ptr0 in range(0, len(self.idx_0),self.half): + bs=self.half + if self.half + ptr0 > len(self.idx_0): + bs = len(self.idx_0) - ptr0 + for ptr1 in range(0, len(self.idx_1),bs): + batch = np.concatenate([ + self.idx_0[ptr0:ptr0 + bs ], + self.idx_1[ptr1:ptr1 + bs] + ]) + np.random.shuffle(batch) + yield batch.tolist() + ptr1 += self.half ptr0 += self.half - ptr1 += self.half def __len__(self): return len(self.idx_0) // self.half From aa91fe4a82e34fbb166dc1d4d8ceaa7ce259bb4d Mon Sep 17 00:00:00 2001 From: hussien Date: Tue, 17 Feb 2026 14:09:38 -0500 Subject: [PATCH 8/8] Modular Design+Logging+MTL --- .gitignore | 1 + tgrag/LLM/Crediability_Prompt.py | 89 +++ tgrag/content_embbeding/open_ai_binary_emb.py | 63 ++ .../mlp_experiments/dataset_loader.py | 540 ++++++++++++++++++ .../mlp_experiments/mlp_modules.py | 318 ++++++++--- .../experiments/mlp_experiments/mlp_train.py | 408 ------------- .../mlp_experiments/mlp_train_classifier.py | 341 +++-------- .../mlp_experiments/mlp_train_multihead.py | 92 +++ .../mlp_experiments/mlp_train_regressor.py | 133 +++++ .../regressor_transfer_inference.py | 108 ++++ tgrag/experiments/mlp_experiments/samplers.py | 63 +- tgrag/experiments/mlp_experiments/utils.py | 46 +- tgrag/utils/logger.py | 3 +- 13 files changed, 1449 insertions(+), 756 deletions(-) create mode 100644 tgrag/LLM/Crediability_Prompt.py create mode 100644 tgrag/content_embbeding/open_ai_binary_emb.py create mode 100644 tgrag/experiments/mlp_experiments/dataset_loader.py delete mode 100644 tgrag/experiments/mlp_experiments/mlp_train.py create mode 100644 tgrag/experiments/mlp_experiments/mlp_train_multihead.py create mode 100644 tgrag/experiments/mlp_experiments/mlp_train_regressor.py create mode 100644 tgrag/experiments/mlp_experiments/regressor_transfer_inference.py diff --git a/.gitignore b/.gitignore index 370f7882..54daf49b 100644 --- a/.gitignore +++ b/.gitignore @@ -200,3 +200,4 @@ bash_scripts/jdk-25_linux-x64_bin.tar bash_scripts/jdk-17.0.12_linux-x64_bin.tar.gz bash_scripts/jdk-25_linux-x64_bin.tar.gz uv.lock +tgrag/distribution/kl_divergiance.py diff --git a/tgrag/LLM/Crediability_Prompt.py b/tgrag/LLM/Crediability_Prompt.py new file mode 100644 index 00000000..039997be --- /dev/null +++ b/tgrag/LLM/Crediability_Prompt.py @@ -0,0 +1,89 @@ +import asyncio +from openai import OpenAI,AsyncOpenAI +from dotenv import load_dotenv +from glob import glob +import os +import json +import pandas as pd +import pickle +from tqdm import tqdm + +async def get_completion_pc1(client, prompt): + completion = await client.chat.completions.create( + model="gpt-5", + messages=[ + {"role": "developer", "content": "You are a web domain credibility assessment assistant. Given a web domain and a sample content, assign a credibility score from 0 to 10 where with 10 being most credible and 0 being least credible. "}, + {"role": "user", "content":prompt } + ] + ) + # print(completion.choices[0].message.content) + return completion.choices[0].message.content + +async def get_completion_mbfc(client, prompt): + completion = await client.chat.completions.create( + model="gpt-5", + messages=[ + {"role": "developer", "content": "You are a web domain credibility assessment assistant. Given a web domain and a sample content, assign a Media Bias/Fact Check credibility score from 0 to 10 where with 10 being most credible and 0 being least credible. "}, + {"role": "user", "content":prompt } + ] + ) + # print(completion.choices[0].message.content) + return completion.choices[0].message.content + +async def main(): + load_dotenv() + # print(os.getenv("OPENAI_API_KEY")) + client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY")) # Replace with your actual key + + # for month in ['oct','nov','dec']: + target="mbfc" + domain_ratings_df=pd.read_csv("/shared_mnt/github_repos/CrediGraph/data/dqr/domain_ratings.csv") + domain_ratings_dict=dict(zip(domain_ratings_df["domain"],domain_ratings_df[target])) + test_set_domains=pd.read_csv(f"/shared_mnt/github_repos/CrediGraph/plots/dqr_testset_nov_{target}_text_embeddingTE3L.csv")["domain"].tolist() + + for month in ['oct','nov','dec']: + domains_content_df=pd.read_csv(f"/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_{month}_domains_content.csv") + # print(domains_content_df.columns) + print(f"month={month}") + if "Domain_Name" in domains_content_df.columns: + domains_content_df.rename(columns={'Domain_Name': 'url'}, inplace=True) + domains_content_df=domains_content_df[domains_content_df["url"].isin(test_set_domains)] + domains_content_df[target]=domains_content_df["url"].apply(lambda x:domain_ratings_dict[x]) + domains_content_df[f"{target}_rounded"]=round(domains_content_df[target],2) + domains_content_df=domains_content_df.sort_values(by=[target]) + answers_dict={} + prompts_lst=[] + for i,row in domains_content_df.iterrows(): + if "title" in domains_content_df.columns: + domain_content=f"""domain name: {str(row["url"])} + domain titile: {row["title"]} + domain description: { row["desc"]} + domain content: {str(row["text"])[0:10000]}""" + else: + domain_content=f"""domain name: {str(row["url"])} + domain content: {str(row["txt"])[0:10000]}""" + if target=="pc1": + message=f"Assign a credibility score from 0 to 10, where 10 is being most credible and 0 is being least credible.\nReturn your answer in format Score=.\n\n {domain_content}" + get_completion=get_completion_pc1 + elif target =="mbfc": + message=f"Assign a Media Bias/Fact Check (MBFC) credibility score from 0 to 10, where 10 is being most credible and 0 is being least credible. the possible scores are 0,2,4,6,8,10. \nReturn your answer in format Score=.\n\n {domain_content}" + get_completion=get_completion_mbfc + + + prompts_lst.append([row["url"],row[target],message]) + batch_size=50 + answers_dict={} + for i in tqdm(range(0,len(prompts_lst),10)): + # print("i=",i) + tasks = [get_completion(client, prompt[2]) for prompt in prompts_lst[i:i+batch_size]] + results = await asyncio.gather(*tasks) + # print("results=",results) + for bs_i in range(len(results)): + answers_dict[prompts_lst[i+bs_i][0]]=[prompts_lst[i+bs_i][1],prompts_lst[i+bs_i][2],results[bs_i]] + + file_name=f'dqr_{month}_{target}_gpt5_chat_results.pkl' + with open(file_name, 'wb') as f: + pickle.dump(answers_dict, f) + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/tgrag/content_embbeding/open_ai_binary_emb.py b/tgrag/content_embbeding/open_ai_binary_emb.py new file mode 100644 index 00000000..42c8c794 --- /dev/null +++ b/tgrag/content_embbeding/open_ai_binary_emb.py @@ -0,0 +1,63 @@ +import asyncio +from openai import OpenAI,AsyncOpenAI +from dotenv import load_dotenv +from glob import glob +import os +import json +import pandas as pd +import pickle +from tqdm import tqdm +import sys +parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) +sys.path.insert(0, parent_dir) +import experiments +from experiments.mlp_experiments.utils import search_parquet_duckdb +import pickle +dotenv_path="/home/mila/a/abdallah/scratch/hsh_projects/config_keys/.env" +data_path="/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/data" +out_path="/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/data/scrapedContent/weaklabels/TE3L" +text_max_length=4500 +def open_ai_emb(client,docs_lst,domains_lst,bs=100,out_batch_prefix="binary_TE3L_domains_woc_emb_",model_name="text-embedding-3-large"): + embd_dict={} + for batch_idx,doc_idx in enumerate(tqdm(range(0,len(docs_lst),bs))): + response = client.embeddings.create( + model=model_name, + input=docs_lst[doc_idx:doc_idx+bs]) + for url_idx,emb in enumerate(response.data): + embd_dict[domains_lst[doc_idx+url_idx]]=emb.embedding + + if (batch_idx+1)%100==0 or doc_idx+bs>=len(docs_lst): + file_name=f'{out_path}/{out_batch_prefix}_{len(embd_dict[list(embd_dict.keys())[0]])}_batch_{batch_idx}.pkl' + with open(file_name, 'wb') as f: + pickle.dump(embd_dict, f) + del embd_dict + embd_dict={} + +def emb_doamins_wc(client): + domain_wc_df=pd.read_csv(f"{data_path}/scrapedContent/weaklabels/weaklabel_domains_with_content.csv") + docs_lst=["Web Domain Name:"+elem[0:text_max_length] for elem in domain_wc_df["text"].to_list()] + domains_lst=domain_wc_df["domain"] + open_ai_emb(client,docs_lst,domains_lst,bs=100,out_batch_prefix="binary_TE3L_domains_wc_emb") +def emb_doamins_woc(client): + domain_woc_df=pd.read_csv(f"{data_path}/scrapedContent/weaklabels/weaklabel_domains_without_content.csv") + docs_lst=["Web Domain Name:"+elem[0:text_max_length] for elem in domain_woc_df["domain"].to_list()] + domains_lst=domain_woc_df["domain"] + open_ai_emb(client,docs_lst,domains_lst,bs=100,out_batch_prefix="binary_TE3L_domains_woc_emb") +def emb_month_doamins_woc(client): + for month in ["Oct","Nov","Dec"]: + print(f"########## Emb {month} Month Content ###########") + file_path=f"{data_path}/weaksupervision/weaksupervision_content_{month}2024.parquet" + month_content_df=search_parquet_duckdb(file_path, col=None,q_domains=None,max_memory="8GB",schema=None) + docs_lst=["Web Domain Name:"+month_content_df["pages"].tolist()[0][0]['wet_record_txt'][0:text_max_length] for elem in month_content_df["pages"].tolist()] + domains_lst=month_content_df["domain"] + del month_content_df + open_ai_emb(client,docs_lst,domains_lst,bs=100,out_batch_prefix=f"binary_TE3L_domains_{month}_emb") + +def main(): + load_dotenv(dotenv_path) + client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) # Replace with your actual key + # emb_doamins_wc(client) + # emb_doamins_woc(client) + emb_month_doamins_woc(client) +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/dataset_loader.py b/tgrag/experiments/mlp_experiments/dataset_loader.py new file mode 100644 index 00000000..33ed9e32 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/dataset_loader.py @@ -0,0 +1,540 @@ +from utils import search_parquet_duckdb,normalize_embeddings,train_valid_test_split,resize_emb +import pandas as pd +import pickle +import logging +agg_months_dict={"oct":["oct"], + "nov":["oct","nov"], + "dec":["oct","nov","dec"]} +class DomainRel(object): + @staticmethod + def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg",gnn_encoder="text",normalize=False,emb_dim=256,original_emb_dim=256): + months_emb_lst = [] + + for month in month_lst: + if embed_type == "GNN_GAT": + if gnn_encoder=="RNI": + with open(f'{path}/gnn_embedding/{gnn_encoder}/{month}_binary_labelled_set_domain_rni_embeddings_updated_balanced.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif gnn_encoder=="text": + file_path=f'{path}/gnn_embedding/{gnn_encoder}/{month}_binary_labelled_set_domain_from_text_embeddings_updated_balanced.parquet' + logging.info(f"GNN emb file path={file_path}") + embd_dict=search_parquet_duckdb(file_path, col=None,q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) + elif embed_type == "text": + embd_dict=DomainRel.load_emb_dict_from_parquet(embed_type, path, model_name, month,normalize=False,emb_dim=emb_dim, original_emb_dim=original_emb_dim) + if normalize: + embd_dict=normalize_embeddings(embd_dict) + months_emb_lst.append(embd_dict) + + common_domains_set=set(months_emb_lst[0].keys()) + for lst in months_emb_lst[1:]: + common_domains_set = common_domains_set.intersection(lst.keys()) + + diff_domains_set=set(months_emb_lst[-1].keys())-common_domains_set + for key in diff_domains_set: + if agg == "cat": + months_emb_lst[-1][key].extend(months_emb_lst[-1][key]*len(months_emb_lst)) + + for key in common_domains_set: + for i in range(0, len(months_emb_lst)-1): + if agg == "cat": + months_emb_lst[-1][key].extend(months_emb_lst[i][key]) + elif agg == "min": + months_emb_lst[-1][key] = [min(a, b) for a, b in zip(months_emb_lst[-1][key], months_emb_lst[i][key])] + elif agg == "max": + months_emb_lst[-1][key] = [max(a, b) for a, b in zip(months_emb_lst[-1][key], months_emb_lst[i][key])] + elif agg == "avg": + months_emb_lst[-1][key] = [(a + b) / 2 for a, b in zip(months_emb_lst[-1][key], months_emb_lst[i][key])] + + # concat_dict = {k: v for k, v in months_emb_lst[-1].items() if k in common_domains_set} + # logging.info(f"concat Nmonth emb size={len(concat_dict[list(concat_dict.keys())[0]])}") + # logging.info(f"len of keys={len(concat_dict.keys())}") + return months_emb_lst[-1] + + @staticmethod + def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): + months_emb_PhishTank_lst = [] + months_emb_URLhaus_lst = [] + months_emb_legit_lst = [] + for month in month_lst: + with open(f'{path}/PhishTank_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_PhishTank_lst.append(pickle.load(f)) + with open(f'{path}/URLHaus_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_URLhaus_lst.append(pickle.load(f)) + with open(f'{path}/IP2Location_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_legit_lst.append(pickle.load(f)) + + for ds_months in [months_emb_PhishTank_lst, months_emb_URLhaus_lst, months_emb_legit_lst]: + for key in ds_months[0].keys(): + for i in range(1, len(ds_months)): # loop on dataset months + if key in ds_months[i]: + if agg == "concat": + ds_months[0][key].extend(ds_months[i][key]) + # logging.info(len(ds_months[0][key])) + elif agg == "min": + ds_months[0][key] = [min(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "max": + ds_months[0][key] = [max(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "avg": + ds_months[0][key] = [(a + b) / 2 for a, b in zip(ds_months[0][key], ds_months[i][key])] + # logging.info(len(ds_months[0][key])) + return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] + + @staticmethod + def load_emb_dict(embed_type, path="../../../data",pickle_name=None, model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=8192,normalize=False,gnn_encoder="RNI"): + if pickle_name: + with open(f'{path}/{pickle_name}', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "domainName": + with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + if gnn_encoder=="RNI": + with open(f'{path}/gnn_embedding/{gnn_encoder}/{month}_binary_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif gnn_encoder=="text": + file_path=f'{path}/gnn_embedding/{gnn_encoder}/{month}_binary_labelled_set_domain_from_text_embeddings_updated_balanced.parquet' + logging.info(f"GNN emb file path={file_path}") + embd_dict=search_parquet_duckdb(file_path, col=None,q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) + + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'][0:emb_dim] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1][0:emb_dim] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) + return embd_dict + @staticmethod + def load_emb_dict_from_parquet(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=8192,normalize=False,original_emb_dim=1024): + embd_dict=None + if embed_type == "text": + embd_dict=search_parquet_duckdb(f'{path}/weak_content_emb_{month}2024_{model_name}_{original_emb_dim}.parquet', col="domain",q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) + elif embed_type == "GNN_GAT": + with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'][0:emb_dim] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1][0:emb_dim] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) + return embd_dict + @staticmethod + def load_splits(path): + test_domains_df=search_parquet_duckdb(f'{path}/all_splits/balanced/test_domains.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + test_domains_df['domain']=test_domains_df['domain'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + + valid_domains_df=search_parquet_duckdb(f'{path}/all_splits/balanced/val_domains.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + valid_domains_df['domain']=valid_domains_df['domain'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + + train_domains_df=search_parquet_duckdb(f'{path}/all_splits/balanced/train_domains.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + train_domains_df['domain']=train_domains_df['domain'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + return train_domains_df,valid_domains_df,test_domains_df + + @staticmethod + def load_run_embeddings(args): + global agg_months_dict + ############## Load text embeddings and labels ############### + if args.emb_model=="embeddingTE3L": + # args.emb_model="embeddinggemma-300m" + args.emb_model="Qwen3-Embedding-0.6B" + + full_emb_dict =DomainRel.load_emb_dict(args.embed_type, args.domainRel_text_emb_path, pickle_name=f"weak_content_emb_{args.emb_model}_{args.original_emb_dim}.pkl",emb_dim=args.emb_dim,normalize=False) + if args.agg_text_emb: + month_emb_dict = DomainRel.load_agg_Nmonth_emb_dict("text",args.domainRel_gnn_emb_path,model_name=args.emb_model, agg=args.agg_function,gnn_encoder=args.gnn_encoder,month_lst=agg_months_dict[args.month],emb_dim=args.emb_dim, original_emb_dim=args.original_emb_dim) + else: + month_emb_dict = DomainRel.load_emb_dict_from_parquet(args.embed_type, args.domainRel_text_emb_path, args.emb_model, args.month,normalize=False,emb_dim=args.emb_dim, original_emb_dim=args.original_emb_dim) + + weaklabeles_df = pd.read_csv(f"{args.domainRel_path}/weaklabels.csv") + weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(full_emb_dict)] + weaklabeles_df = weaklabeles_df.reset_index(drop=True) + text_emb_dict={} + text_emb_dict.update(month_emb_dict) + text_emb_dict.update({k:v for k,v in full_emb_dict.items() if k not in month_emb_dict}) + acc_lst,f1_lst=[],[] + ############### filter by the GNN graph node splits ################### + gnn_emb_dict = None + gnn_emb_dict = DomainRel.load_emb_dict("GNN_GAT", args.domainRel_gnn_emb_path,month=args.month,gnn_encoder=args.gnn_encoder) + gnn_emb_dict={".".join(k.split(".")[::-1]):v for k,v in gnn_emb_dict.items()} + ######### handel reversed domains ################ + missing_domains_set=set(gnn_emb_dict.keys())-set(weaklabeles_df[weaklabeles_df["domain"].isin(gnn_emb_dict.keys())]["domain"]) + for k in missing_domains_set: + gnn_emb_dict[".".join(k.split(".")[::-1])]=gnn_emb_dict[k] + + missing_domains_set=set(gnn_emb_dict.keys())-set(weaklabeles_df[weaklabeles_df["domain"].isin(gnn_emb_dict.keys())]["domain"]) + weaklabeles_df=weaklabeles_df[weaklabeles_df["domain"].isin(gnn_emb_dict.keys())] + logging.info(f"len missing domains set={len(missing_domains_set)}") + ############### Load ################### + if args.filter_by_GNN_nodes: + train_domains_df,valid_domains_df,test_domains_df=DomainRel.load_splits(args.domainRel_path) + test_domains_set=set(test_domains_df['domain']) + valid_domains_set=set(valid_domains_df['domain']) + train_domains_set=set(train_domains_df['domain']) + # filter_by_domains_set=test_domains_set.union(valid_domains_set).union(train_domains_set) + logging.info(f"test set labels count ={weaklabeles_df[weaklabeles_df["domain"].isin(test_domains_df['domain'])]["weak_label"].value_counts()}") + logging.info(f"valid set labels count ={weaklabeles_df[weaklabeles_df["domain"].isin(valid_domains_df['domain'])]["weak_label"].value_counts()}") + logging.info(f"train set labels count ={weaklabeles_df[weaklabeles_df["domain"].isin(train_domains_df['domain'])]["weak_label"].value_counts()}") + ################### GNN Embedding ############# + topic_iptc_emb_dict = None + if args.use_gnn_emb: + if args.agg_month_emb: + gnn_emb_dict = DomainRel.load_agg_Nmonth_emb_dict("GNN_GAT",args.domainRel_gnn_emb_path, agg=args.agg_function,gnn_encoder=args.gnn_encoder,month_lst=agg_months_dict[args.month]) + gnn_emb_dict={".".join(k.split(".")[::-1]):v for k,v in gnn_emb_dict.items()} + elif not gnn_emb_dict: + gnn_emb_dict = DomainRel.load_emb_dict("GNN_GAT", args.domainRel_gnn_emb_path,month=args.month,gnn_encoder=args.gnn_encoder) + gnn_emb_dict={".".join(k.split(".")[::-1]):v for k,v in gnn_emb_dict.items()} + weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(gnn_emb_dict.keys())] + weaklabeles_df = weaklabeles_df.reset_index(drop=True) + else: + gnn_emb_dict=None + ############### Split ##################### + if args.filter_by_GNN_nodes: + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.domainRel_target, weaklabeles_df,key='domain',test_valid_size=args.test_valid_size,regressor=False,train_lst=train_domains_set,valid_lst=valid_domains_set,test_lst=test_domains_set) + else: + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.domainRel_target, weaklabeles_df,key='domain',test_valid_size=args.test_valid_size,regressor=False) + + logging.info(f"len(X_train)={len(X_train)}\tlen(X_valid)={len(X_valid)}\tlen(X_test)={len(X_test)}\t") + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.domainRel_target, X_train, X_valid,X_test, gnn_emb=gnn_emb_dict,topic_emb=topic_iptc_emb_dict,trim_to=args.emb_dim) + logging.info(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") + return X_train, y_train, X_valid, y_valid, X_test, y_test,X_train_feat, X_valid_feat, X_test_feat +class DQR (object): + @staticmethod + def load_emb_dict(embed_type, path="../../../data",pickle_name=None, model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=256,normalize=False,gnn_encoder="RNI"): + if pickle_name: + with open(f'{path}/{pickle_name}', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "IPTC_Topic_emb": + with open(f'IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl','rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "domainName": + with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + if gnn_encoder=="RNI": + with open(f'{path}/gnn_embedding/RNI/{target}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif gnn_encoder=="text": + file_path=f'{path}/gnn_embedding/text/{target}/{month}_dqr_domain_gat_from_text_embeddings_updated.parquet' + logging.info(f"GNN emb file path={file_path}") + embd_dict=search_parquet_duckdb(file_path, col=None,q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) + + elif embed_type == "IPTC_Topic": + with open(f'IPTCTopicModeling/dqr_IPTC-news-topic_scores.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "IPTC_Topic_freq": + with open(f'IPTCTopicModeling/dqr_topics_frequency_norm_dict.pkl','rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "IPTC_Topic_emb": + with open(f'IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl','rb') as f: + embd_dict = pickle.load(f) + # logging.info(list(embd_dict.keys())[0],embd_dict[list(embd_dict.keys())[0]]) + elif embed_type == "3Feat": + with open(f'/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_3Feat_dict.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "3Feat2": + with open(f'/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_3Feat_dict2.pkl', 'rb') as f: + embd_dict = pickle.load(f) + + elif embed_type == "TFIDF": + # with open(f'{path}/dqr_TFIDF_emb.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_8465.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_19437.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + if month == "dec": + with open(f'{path}/dqr_dec_TFIDF_weaksupervision_emb_222755.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif month == "nov": + with open(f'{path}/dqr_nov_TFIDF_weaksupervision_emb_258729.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif month == "oct": + with open(f'{path}/dqr_oct_TFIDF_weaksupervision_emb_19085.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "PASTEL": + with open(f'{path}/dqr_pastel_dict.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "PASTEL_hasContent": + with open(f'{path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: + embd_dict = pickle.load(f) + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) + return embd_dict + + @staticmethod + def load_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", + target="pc1", gnn_emb=None, agg=None): + embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = {}, {}, {} + if embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_768.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/cc_dec_2024_phishtank_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: + embd_dict_phishtank = pickle.load(f) + with open(f'{path}/cc_dec_2024_URLhaus_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: + embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/cc_dec_2024_PhishDataset_legit_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: + embd_dict_PhishDataset_legit = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/phishtank_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: + embd_dict_phishtank = pickle.load(f) + with open(f'{path}/URLhaus_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: + embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/PhishDataset_legit_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: + embd_dict_PhishDataset_legit = pickle.load(f) + + if gnn_emb == True: + logging.info("len of embd_dict_phishtank before appending GNN=", + len(embd_dict_phishtank[list(embd_dict_phishtank.keys())[0]])) + if agg is None: + with open(f'{path}/PhishTank_{target}_rni_embeddings.pkl', 'rb') as f: + gnn_embd_dict_phishtank = pickle.load(f) + with open(f'{path}/URLHaus_{target}_rni_embeddings.pkl', 'rb') as f: + gnn_embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/IP2Location_{target}_rni_embeddings.pkl', 'rb') as f: + gnn_embd_dict_PhishDataset_legit = pickle.load(f) + else: + gnn_embd_dict_phishtank, gnn_embd_dict_URLhaus, gnn_embd_dict_PhishDataset_legit = load_agg_Nmonth_weaksupervision_emb_dict( + embed_type, path, model_name, month_lst=["dec", "nov", "oct"], target=target, agg=agg) + + ############# Append Emb ############# + for k in embd_dict_phishtank: + embd_dict_phishtank[k] = gnn_embd_dict_phishtank[k] + embd_dict_phishtank[k] + for k in embd_dict_URLhaus: + embd_dict_URLhaus[k] = gnn_embd_dict_URLhaus[k] + embd_dict_URLhaus[k] + for k in embd_dict_PhishDataset_legit: + embd_dict_PhishDataset_legit[k] = gnn_embd_dict_PhishDataset_legit[k] + embd_dict_PhishDataset_legit[k] + logging.info("len of embd_dict_phishtank after appending GNN=", + len(embd_dict_phishtank[list(embd_dict_phishtank.keys())[0]])) + + + elif embed_type == "domainName": + with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "TFIDF": + # with open(f'{path}/dqr_TFIDF_emb.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_8465.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_dec_TFIDF_emb_19437.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + # with open(f'{path}/dqr_TFIDF_emb_19437.pkl', 'rb') as f: + # embd_dict=pickle.load(f) + emb_size = "222755" if month == "dec" else "258729" if month == "nov" else "19085" + with open(f'{path}/phishtank_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: + embd_dict_phishtank = pickle.load(f) + with open(f'{path}/URLhaus_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: + embd_dict_URLhaus = pickle.load(f) + with open(f'{path}/phishDataset_legit_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: + embd_dict_PhishDataset_legit = pickle.load(f) + + return embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit + + @staticmethod + def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["oct", "nov", "dec"], target="pc1", agg="avg",emb_dim=256,normalize=False,gnn_encoder="text",original_emb_dim=256): + months_emb_lst = [] + for month in month_lst: + if embed_type == "text": + if model_name == "embeddinggemma-300m": + with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-0.6B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingQwen3-8B": + with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif model_name == "embeddingTE3L": + with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif embed_type == "GNN_GAT": + if gnn_encoder=="RNI": + with open(f'{path}/gnn_embedding/RNI/{target}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: + embd_dict = pickle.load(f) + elif gnn_encoder=="text": + file_path=f'{path}/gnn_embedding/text/{target}/{month}_dqr_domain_gat_from_text_embeddings_updated.parquet' + embd_dict=search_parquet_duckdb(file_path, col=None,q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) + + if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) + embd_dict={ k:v[0]['emb'] for k,v in embd_dict.items()} + elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) + embd_dict={ k:v[0][1] for k,v in embd_dict.items()} + if normalize: + embd_dict=normalize_embeddings(embd_dict) + months_emb_lst.append(embd_dict) + + common_domains_set=set(months_emb_lst[0].keys()) + for lst in months_emb_lst[1:]: + common_domains_set = common_domains_set.intersection(lst.keys()) + + diff_domains_set=set(months_emb_lst[-1].keys())-common_domains_set + for key in diff_domains_set: + if agg == "cat": + months_emb_lst[-1][key].extend(months_emb_lst[-1][key]*len(months_emb_lst)) + + for key in common_domains_set: + for i in range(0, len(months_emb_lst)-1): + if agg == "cat": + months_emb_lst[-1][key].extend(months_emb_lst[i][key]) + elif agg == "min": + months_emb_lst[-1][key] = [min(a, b) for a, b in zip(months_emb_lst[-1][key], months_emb_lst[i][key])] + elif agg == "max": + months_emb_lst[-1][key] = [max(a, b) for a, b in zip(months_emb_lst[-1][key], months_emb_lst[i][key])] + elif agg == "avg": + months_emb_lst[-1][key] = [(a + b) / 2 for a, b in zip(months_emb_lst[-1][key], months_emb_lst[i][key])] + + # concat_dict = {k: v for k, v in months_emb_lst[-1].items() if k in common_domains_set} + # logging.info(f"concat Nmonth emb size={len(concat_dict[list(concat_dict.keys())[0]])}") + # logging.info(f"len of keys={len(concat_dict.keys())}") + return months_emb_lst[-1] + + @staticmethod + def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", + month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): + months_emb_PhishTank_lst = [] + months_emb_URLhaus_lst = [] + months_emb_legit_lst = [] + for month in month_lst: + with open(f'{path}/PhishTank_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_PhishTank_lst.append(pickle.load(f)) + with open(f'{path}/URLHaus_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_URLhaus_lst.append(pickle.load(f)) + with open(f'{path}/IP2Location_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: + months_emb_legit_lst.append(pickle.load(f)) + + for ds_months in [months_emb_PhishTank_lst, months_emb_URLhaus_lst, months_emb_legit_lst]: + for key in ds_months[0].keys(): + for i in range(1, len(ds_months)): # loop on dataset months + if key in ds_months[i]: + if agg == "concat": + ds_months[0][key].extend(ds_months[i][key]) + # logging.info(len(ds_months[0][key])) + elif agg == "min": + ds_months[0][key] = [min(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "max": + ds_months[0][key] = [max(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] + elif agg == "avg": + ds_months[0][key] = [(a + b) / 2 for a, b in zip(ds_months[0][key], ds_months[i][key])] + # logging.info(len(ds_months[0][key])) + return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] + + @staticmethod + def load_run_embeddings(args): + global agg_months_dict + ############## Load training data and split ############### + if args.agg_text_emb: + text_emb_dict = DQR.load_agg_Nmonth_emb_dict("text", path= args.dqr_text_emb_path,model_name= args.emb_model,month_lst=agg_months_dict[args.month], agg="avg",normalize=False) + else: + text_emb_dict = DQR.load_emb_dict(args.embed_type,path=args.dqr_text_emb_path, model_name=args.emb_model, month=args.month,normalize=False) + + labeled_11k_df = pd.read_csv(f"{args.dqr_path}/domain_ratings.csv") + labeled_11k_df[f"{args.dqr_target}_norm"]=labeled_11k_df[args.dqr_target].apply(lambda x: round(float(x)*10)) + ######################## Filter by GNN montly nodes #################### + targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") + targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(text_emb_dict)] + ############### filter_by_PASTEL_domains ################### + if 'filter_by_PASTEL_domains' in args and args.filter_by_PASTEL_domains: + pastel_emb_dict = {} + with open(f'{args.dqr_path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: + pastel_emb_dict = pickle.load(f) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(pastel_emb_dict.keys())] + # ############### filter by the 8K GNN Nodes ################### + # if args.filter_by_GNN_nodes: + # labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(targets_nodes_df["domain_rev"])] + ############### filter by the train/val/test domains ################### + test_domains_df=search_parquet_duckdb(f'{args.dqr_path}/splits/test_regression_domains.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + test_domains_df['domain']=test_domains_df['domain'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + filtered_test_df=labeled_11k_df[labeled_11k_df["domain"].isin(test_domains_df['domain'])] + logging.info(f"test set lables count ={len(filtered_test_df),filtered_test_df[f"{args.dqr_target}_norm"].value_counts()}") + + valid_domains_df=search_parquet_duckdb(f'{args.dqr_path}/splits/val_regression_domains.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + valid_domains_df['domain']=valid_domains_df['domain'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + filtered_val_df=labeled_11k_df[labeled_11k_df["domain"].isin(valid_domains_df['domain'])] + logging.info(f"val set lables count ={len(filtered_val_df),filtered_val_df[f"{args.dqr_target}_norm"].value_counts()}") + + train_domains_df=search_parquet_duckdb(f'{args.dqr_path}/splits/train_regression_domains.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) + train_domains_df['domain']=train_domains_df['domain'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) + filtered_train_df=labeled_11k_df[labeled_11k_df["domain"].isin(train_domains_df['domain'])] + logging.info(f"train set lables count ={len(filtered_train_df),filtered_train_df[f"{args.dqr_target}_norm"].value_counts()}") + + test_domains_set=set(test_domains_df['domain']) + valid_domains_set=set(valid_domains_df['domain']) + train_domains_set=set(train_domains_df['domain']) + filter_by_domains_set=test_domains_set.union(valid_domains_set).union(train_domains_set) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(filter_by_domains_set)] + non_exist_domains_df = labeled_11k_df[~labeled_11k_df["domain"].isin(filter_by_domains_set)] + logging.info(f"non_exist_domains_df={non_exist_domains_df}") + labeled_11k_df = labeled_11k_df.reset_index(drop=True) + text_emb_dict={k:v for k,v in text_emb_dict.items() if k in filter_by_domains_set} + + + topic_iptc_emb_dict = None + gnn_emb_dict=None + if args.use_gnn_emb: + if args.agg_month_emb: + agg_months_dict={"oct":["oct"], + "nov":["oct","nov"], + "dec":["oct","nov","dec"]} + gnn_emb_dict = DQR.load_agg_Nmonth_emb_dict("GNN_GAT", args.dqr_gnn_emb_path, agg=args.agg_function,gnn_encoder=args.gnn_encoder,month_lst=agg_months_dict[args.month]) + else: + gnn_emb_dict = DQR.load_emb_dict("GNN_GAT", args.dqr_gnn_emb_path,month=args.month,gnn_encoder=args.gnn_encoder) + labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(gnn_emb_dict.keys())] + if 'use_topic_emb' in args and args.use_topic_emb: + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_freq", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_emb", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("3Feat", args.emb_path) + # topic_iptc_emb_dict = load_emb_dict("3Feat2", args.emb_path) + # gnn_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) + topic_iptc_emb_dict = DQR.load_emb_dict("PASTEL_hasContent", args.dqr_text_emb_path) + + ############ Use 3M fixed Split ################# + X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.dqr_target, labeled_11k_df,key='domain',test_valid_size=args.test_valid_size,regressor=False,train_lst=train_domains_set,valid_lst=valid_domains_set,test_lst=test_domains_set) + ############ Use Startified random split per month ################# + # X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.dqr_target, labeled_11k_df,key='domain',test_valid_size=args.test_valid_size) + ############ resize Emb ################# + X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.dqr_target, X_train, X_valid,X_test, gnn_emb=gnn_emb_dict,topic_emb=topic_iptc_emb_dict,trim_to=args.emb_dim) + logging.info(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") + return X_train, y_train, X_valid, y_valid, X_test, y_test,X_train_feat, X_valid_feat, X_test_feat + diff --git a/tgrag/experiments/mlp_experiments/mlp_modules.py b/tgrag/experiments/mlp_experiments/mlp_modules.py index 709d1b2d..60f313bf 100644 --- a/tgrag/experiments/mlp_experiments/mlp_modules.py +++ b/tgrag/experiments/mlp_experiments/mlp_modules.py @@ -2,6 +2,7 @@ # Required imports # ---------------------------- import torch +import logging import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader, TensorDataset @@ -13,7 +14,7 @@ from sklearn.metrics import log_loss import torch.nn.functional as F from torch import Tensor, nn -from samplers import BalancedBatchSampler, BinaryClassificationDataset +from samplers import BalancedBatchSampler, BinaryClassificationDataset,BalancedRegClsBatchSampler,MultiTaskDataset # ---------------------------- seed = 42 torch.manual_seed(seed) @@ -32,7 +33,7 @@ def train_scikitlearn_regressor(mlp_reg, X_train_feat, Y_train, X_valid_feat, Y_ test_loss.append(mean_squared_error(Y_test, mlp_reg.predict(X_test_feat))) mean_loss.append(mean_squared_error(y_batch, [batch_mean for elem in y_batch])) if early_stopper.step(valid_loss[-1], mlp_reg): - print("Early stopping triggered.") + logging.info("Early stopping triggered.") mlp_reg = early_stopper.restore_best_weights() break return mlp_reg, train_loss, valid_loss, test_loss, mean_loss @@ -51,7 +52,28 @@ def train_scikitlearn_classifier(mlp_clf, X_train_feat, Y_train, X_valid_feat, Y valid_loss.append(log_loss(Y_valid, mlp_clf.predict_proba(X_valid_feat))) test_loss.append(log_loss(Y_test, mlp_clf.predict_proba(X_test_feat))) if early_stopper.step(valid_loss[-1], mlp_clf): - print("Early stopping triggered.") + logging.info("Early stopping triggered.") + mlp_clf = early_stopper.restore_best_weights() + break + return mlp_clf, train_loss, valid_loss, test_loss,None +def train_scikitlearn_classifier_unbalanced(mlp_clf, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, epochs=15): + batch_size, train_loss, valid_loss, test_loss = 5000, [], [], [] + sampler = BalancedBatchSampler(Y_train, batch_size=batch_size) + dataset = BinaryClassificationDataset(X_train_feat, Y_train) + early_stopper = Sklearn_EarlyStopping(patience=5) + all_classes = np.unique(Y_train) + for i in tqdm(range(epochs)): + train_loader = DataLoader(dataset, batch_sampler=sampler) + for batch_idx, (X_batch, y_batch) in enumerate(train_loader): + if i == 0: + mlp_clf.partial_fit(X_batch, y_batch, classes=all_classes) + else: + mlp_clf.partial_fit(X_batch, y_batch) + train_loss.append(mlp_clf.loss_) + valid_loss.append(log_loss(Y_valid, mlp_clf.predict_proba(X_valid_feat))) + test_loss.append(log_loss(Y_test, mlp_clf.predict_proba(X_test_feat))) + if early_stopper.step(valid_loss[-1], mlp_clf): + logging.info("Early stopping triggered.") mlp_clf = early_stopper.restore_best_weights() break return mlp_clf, train_loss, valid_loss, test_loss,None @@ -96,7 +118,6 @@ def step(self, val_loss, model): def restore_best_weights(self): return self.best_state - class MLP3LayersPredictor(nn.Module): def __init__( self, in_dim: int, hidden_dim_multiplier: float = 0.5, out_dim: int = 1 @@ -108,13 +129,16 @@ def __init__( self.lin_node3 = nn.Linear(hidden_dim//2, hidden_dim//8) self.out = nn.Linear(hidden_dim//8, out_dim) - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.lin_node(x) - x = x.relu() + self.activation=nn.ReLU() + # self.activation=nn.GELU() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.lin_node(x) + x = self.activation(x) x = self.lin_node2(x) - x = x.relu() + x = self.activation(x) x = self.lin_node3(x) - x = x.relu() + x = self.activation(x) x = self.out(x) x = x.sigmoid() return x @@ -126,27 +150,31 @@ def __init__( ): super().__init__() hidden_dim = int(hidden_dim_multiplier * in_dim) + # hidden_dim=64 self.lin_node = nn.Linear(in_dim, hidden_dim) self.out = nn.Linear(hidden_dim, out_dim) + self.activation=nn.ReLU() + # self.activation=nn.GELU() def forward(self, x: Tensor) -> Tensor: x = self.lin_node(x) - x = x.relu() + x = self.activation(x) x = self.out(x) return torch.log_softmax(x, dim=-1) def predict(self, x): return self.forward(torch.tensor(x).float()).argmax(dim=-1) - class MLPRegressor(nn.Module): def __init__(self, input_size, hidden_layer_sizes): super(MLPRegressor, self).__init__() + self.activation=nn.ReLU() + # self.activation=nn.GELU() layers = [] layers.append(nn.Linear(input_size, hidden_layer_sizes[0])) - layers.append(nn.ReLU()) + layers.append(self.activation) # Hidden layers for i in range(len(hidden_layer_sizes) - 1): layers.append(nn.Linear(hidden_layer_sizes[i], hidden_layer_sizes[i+1])) - layers.append(nn.ReLU()) + layers.append(self.activation) layers.append(nn.Linear(hidden_layer_sizes[-1], 1)) layers.append(nn.Sigmoid()) self.model = nn.Sequential(*layers) @@ -180,7 +208,7 @@ def train_mlp(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, train_loss_val.backward() optimizer.step() if early_stopper.step(valid_loss[-1], model): - print("Early stopping triggered.") + logging.info("Early stopping triggered.") early_stopper.restore_best_weights(model) break return model, train_loss, valid_loss, test_loss, mean_loss @@ -210,23 +238,19 @@ def train_classifier(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test test_clf_out = model(torch.tensor(X_test_feat).float()) # test_clf_out=test_clf_out.argmax(dim=-1) test_loss_lst.append(criterion_clf(test_clf_out.detach().float().squeeze(), torch.tensor(Y_test).squeeze()).detach().numpy()) - print(f"Epoch={epoch}\t Batch={b}\t train_loss={train_loss_lst[-1]}\t valid_loss={valid_loss_lst[-1]}\t test_loss={test_loss_lst[-1]}") + logging.info(f"Epoch={epoch}\t Batch={b}\t train_loss={train_loss_lst[-1]}\t valid_loss={valid_loss_lst[-1]}\t test_loss={test_loss_lst[-1]}") if early_stopper.step(valid_loss_lst[-1], model): - print("Early stopping triggered.") + logging.info("Early stopping triggered.") early_stopper.restore_best_weights(model) break return model, train_loss_lst, valid_loss_lst, test_loss_lst, None - - def train_classifier_unbalanced(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15,batch_size=int(1e4)): sampler = BalancedBatchSampler(Y_train, batch_size=batch_size) - dataset = BinaryClassificationDataset(X_train_feat, Y_train) - + dataset = BinaryClassificationDataset(X_train_feat, Y_train) # for batch_idx, (X_batch, y_reg) in enumerate(train_loader): - # print(f"Batch {batch_idx}: X_batch shape: {X_batch.shape}, y_reg shape: {y_reg.shape}") - + # logging.info(f"Batch {batch_idx}: X_batch shape: {X_batch.shape}, y_reg shape: {y_reg.shape}") model.train() batch_size, train_loss_lst, valid_loss_lst, test_loss_lst = len(Y_train)//5, [], [], [] optimizer = torch.optim.Adam(model.parameters(), lr=lr) @@ -250,10 +274,10 @@ def train_classifier_unbalanced(model, X_train_feat, Y_train, X_valid_feat, Y_va test_clf_out = model(torch.tensor(X_test_feat).float()) # test_clf_out=test_clf_out.argmax(dim=-1) test_loss_lst.append(criterion_clf(test_clf_out.detach().float().squeeze(), torch.tensor(Y_test).squeeze()).detach().numpy()) - print(f"Epoch={epoch}\t Batch={batch_idx}\t train_loss={train_loss_lst[-1]}\t valid_loss={valid_loss_lst[-1]}\t test_loss={test_loss_lst[-1]}") + logging.info(f"Epoch={epoch}\t Batch={batch_idx}\t train_loss={train_loss_lst[-1]}\t valid_loss={valid_loss_lst[-1]}\t test_loss={test_loss_lst[-1]}") if early_stopper.step(valid_loss_lst[-1], model): - print("Early stopping triggered.") + logging.info("Early stopping triggered.") early_stopper.restore_best_weights(model) break return model, train_loss_lst, valid_loss_lst, test_loss_lst, None @@ -262,63 +286,225 @@ def train_classifier_unbalanced(model, X_train_feat, Y_train, X_valid_feat, Y_va class MultiTaskMLP(nn.Module): def __init__(self, input_dim, num_classes, hidden_dims=[128, 64]): super(MultiTaskMLP, self).__init__() - + self.activation=nn.ReLU() + # self.activation=nn.GELU() # Shared feature extractor layers = [] - in_dim = input_dim - for h in hidden_dims: - layers.append(nn.Linear(in_dim, h)) - layers.append(nn.ReLU()) - # layers.append(nn.Dropout(0.3)) - # layers.append(nn.BatchNorm1d(h)) - in_dim = h + layers.append(nn.Linear(input_dim, hidden_dims[0])) + layers.append(self.activation) + for idx in range(1,len(hidden_dims)): + layers.append(nn.Linear(hidden_dims[idx-1], hidden_dims[idx])) + layers.append(self.activation) self.trunk = nn.Sequential(*layers) # Task-specific heads - self.reg_head = nn.Linear(in_dim, 1) # Regression - self.cls_head = nn.Linear(in_dim, num_classes) # Multi-class classification + self.reg_pre_head = nn.Linear(hidden_dims[-1], hidden_dims[-1]//2) + self.reg_head = nn.Linear(hidden_dims[-1]//2, 1) # Regression + + self.cls_pre_head = nn.Linear(hidden_dims[-1], hidden_dims[-1]//2) + self.cls_head = nn.Linear(hidden_dims[-1]//2, num_classes) # Multi-class classification + def forward(self, x,y): + z_x = self.trunk(x) + z_y = self.trunk(y) + + reg_out = self.reg_pre_head(z_x) # (batch, 1) + reg_out = self.reg_head(reg_out) # (batch, 1) + reg_out=reg_out.sigmoid() + + cls_logits = self.cls_pre_head(z_y) # (batch, num_classes) + cls_logits = self.cls_head(cls_logits) + cls_pred=torch.log_softmax(cls_logits, dim=-1) + + return reg_out, cls_logits, cls_pred + + def predict(self, x,y): + reg_out, cls_logits, cls_pred = self.forward(torch.tensor(x).float(),torch.tensor(y).float()) + cls_pred=cls_pred.argmax(dim=-1) + return reg_out.detach().squeeze(1).numpy(),cls_pred + + +class MLPHead(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dims=[128], dropout=0.2): + super().__init__() + layers = [] + d = in_dim + for h in hidden_dims: + layers.append(nn.Linear(d, h)) + layers.append(nn.ReLU()) + # layers.append(nn.Dropout(dropout)) + d = h + layers.append(nn.Linear(d, out_dim)) + self.net = nn.Sequential(*layers) def forward(self, x): + return self.net(x) + +class MultiTaskMLP(nn.Module): + def __init__(self, input_dim, num_classes,hidden_dims=[32]): + super().__init__() + + # Shared trunk + self.trunk = nn.Sequential( + nn.Linear(input_dim, 128), + nn.ReLU(), + # nn.Linear(128, 64), + # nn.ReLU(), + ) + + # Task-specific deep heads + self.reg_head = MLPHead(128, 1) + self.cls_head = MLPHead(128, num_classes) + + def forward(self, x, task=None): z = self.trunk(x) - reg_out = self.reg_head(z) # (batch, 1) - cls_logits = self.cls_head(z) # (batch, num_classes) - cls_pred = torch.argmax(cls_logits, dim=1) - return reg_out, cls_logits, cls_pred + if task == "reg": + reg_out= self.reg_head(z) + reg_out=reg_out.sigmoid() + return reg_out + elif task == "cls": + clf_out=self.cls_head(z) + return torch.log_softmax(clf_out, dim=-1) + else: + # default: return both + return self.reg_head(z).sigmoid(), torch.log_softmax(self.cls_head(z),dim=-1) + def predict(self, x,task=None): + out= self.forward(torch.tensor(x).float(),task) + if task == "cls": + out=out.argmax(dim=-1) + return out - def predict(self, x): - reg_out, cls_logits, cls_pred = self.forward(torch.tensor(x)) - return reg_out.detach().squeeze(1).numpy() +class URLLoss(nn.Module): + def __init__(self): + super().__init__() + # two learnable parameters + self.alpha = nn.Parameter(torch.zeros(2)) # [cls, reg] + def forward(self, loss_cls, loss_reg): + # softmax to enforce constraint + weights = torch.softmax(self.alpha, dim=0) + w_cls = weights[0] + w_reg = weights[1] + total_loss = loss_cls / w_cls + loss_reg / w_reg + return total_loss -def train_multihead(model, X_train_feat, Y_train, X_valid_feat, Y_valid, X_test_feat, Y_test, lr=1e-4, epochs=15, - num_classes=3, w_reg=0.8, w_cls=0.2): - batch_size, train_loss, valid_loss, test_loss, mean_loss = 5000, [], [], [], [] +def train_multihead_unbalanced(model, reg_X_train_feat, reg_Y_train, reg_X_valid_feat, reg_Y_valid, reg_X_test_feat, reg_Y_test, + cls_X_train_feat, cls_Y_train, cls_X_valid_feat, cls_Y_valid, cls_X_test_feat, cls_Y_test, lr=1e-4, epochs=15,batch_size=int(1e4),use_URL_Loss=False): + sampler = BalancedRegClsBatchSampler(reg_Y_train,cls_Y_train, batch_size=batch_size) + dataset = MultiTaskDataset(reg_X_train_feat,cls_X_train_feat, reg_Y_train,cls_Y_train) + model.train() + batch_size, train_loss_lst, valid_loss_lst, test_loss_lst,mean_loss_lst = len(cls_Y_train)//5, [], [], [],[] + train_loss_clf_lst,valid_loss_clf_lst, test_loss_clf_lst=[],[],[] + + if use_URL_Loss: + url_loss = URLLoss() + optimizer=torch.optim.Adam([{"params": model.parameters()},{"params": url_loss.parameters(), "lr": 1e-1}],lr=lr) + else: + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion_clf =F.nll_loss + criterion_reg = nn.MSELoss() + early_stopper = EarlyStopping(patience=5) + + for epoch in tqdm(range(epochs)): + train_loader = DataLoader(dataset, batch_sampler=sampler) + for batch_idx, (reg_X_batch,cls_X_batch,reg_y,cls_y) in enumerate(train_loader): + optimizer.zero_grad() + reg_out= model(torch.tensor(reg_X_batch).float(),task="reg") + reg_loss= criterion_reg(reg_out.squeeze(1), torch.tensor(reg_y)) + reg_loss_val=reg_loss.detach().numpy() + # if epochs>(epoch+1): + # train_loss.backward() + # optimizer.step() + # optimizer.zero_grad() + + clf_out= model(torch.tensor(cls_X_batch).float(),task="cls") + clf_loss= criterion_clf(clf_out.float(), torch.tensor(cls_y).long()) + if use_URL_Loss: + loss = url_loss(reg_loss, clf_loss) + else: + loss = 0.2 * reg_loss + 0.8 * clf_loss + if epochs>(epoch+1): + loss.backward() + optimizer.step() + + with torch.no_grad(): + clf_loss_val=reg_loss.detach().numpy() + train_loss_lst.append(reg_loss_val) + train_loss_clf_lst.append(clf_loss_val) + if use_URL_Loss: + logging.info(f"URL Loss Learned Weights(cls,reg)={torch.softmax(url_loss.alpha, dim=0)}") + + val_cls_out = model(torch.tensor(cls_X_valid_feat).float(),task="cls") + valid_loss_clf_lst.append(criterion_clf(val_cls_out.detach().float().squeeze(), torch.tensor(cls_Y_valid).squeeze()).detach().numpy()) + + val_reg_out = model(torch.tensor(reg_X_valid_feat).float(),task="reg") + valid_loss_lst.append(criterion_reg(val_reg_out.detach().float().squeeze(1), torch.tensor(reg_Y_valid))) + + test_cls_out = model(torch.tensor(cls_X_test_feat).float(),task="cls") + test_loss_clf_lst.append(criterion_clf(test_cls_out.detach().float().squeeze(), torch.tensor(cls_Y_test).squeeze()).detach().numpy()) + + test_reg_out = model(torch.tensor(reg_X_test_feat).float(),task="reg") + test_loss_lst.append(criterion_reg(test_reg_out.detach().float().squeeze(1), torch.tensor(reg_Y_test))) + + logging.info(f"Epoch={epoch}\t train_loss={train_loss_lst[-1]}||\t val_loss_reg={valid_loss_lst[-1]}\t val_loss_clf={valid_loss_clf_lst[-1]}\t|| test_loss={test_loss_lst[-1]}\t test_loss={test_loss_clf_lst[-1]}") + + if early_stopper.step(valid_loss_lst[-1]*.5+valid_loss_clf_lst[-1]*.5, model): + logging.info("Early stopping triggered.") + early_stopper.restore_best_weights(model) + break + return model,train_loss_lst,train_loss_clf_lst,valid_loss_lst,valid_loss_clf_lst, test_loss_lst,test_loss_clf_lst, mean_loss_lst + +def train_multihead_v2(model, X_train_feat_reg, Y_train_reg, X_valid_feat_reg, Y_valid_reg, X_test_feat_reg, Y_test_reg, + X_train_feat_clf, Y_train_clf, X_valid_feat_clf, Y_valid_clf, X_test_feat_clf, Y_test_clf, lr=1e-4, epochs=15, + num_classes=3, w_reg=0.5, w_cls=0.5): + batch_size, train_loss_lst, valid_loss_lst, test_loss_lst, mean_loss_lst = 5000, [], [], [], [] + train_loss_clf_lst,valid_loss_clf_lst, test_loss_clf_lst=[],[],[] optimizer = torch.optim.Adam(model.parameters(), lr=lr) criterion_reg = nn.MSELoss() - criterion_cls = nn.CrossEntropyLoss() + criterion_clf = F.nll_loss w_reg = w_reg w_cls = w_cls + sampler = BalancedBatchSampler(Y_train_clf, batch_size=batch_size) + dataset = BinaryClassificationDataset(X_train_feat_clf, Y_train_clf) + model.train() + early_stopper = EarlyStopping(patience=5) for epoch in tqdm(range(epochs)): - for b in range(0, len(Y_train), batch_size): - X_batch, y_reg = X_train_feat[b:b + batch_size], Y_train[b:b + batch_size] - y_cls = torch.tensor([int(((elem * 10) % 10) / (num_classes + 1)) for elem in y_reg], dtype=torch.long) + for b in range(0, len(Y_train_reg), batch_size): + X_batch_reg, y_reg = X_train_feat_reg[b:b + batch_size], Y_train_reg[b:b + batch_size] + train_loader_clf = DataLoader(dataset, batch_sampler=sampler) batch_mean = sum(y_reg) / len(y_reg) - reg_out, cls_logits, cls_pred = model(torch.tensor(X_batch)) - # Loss calculations - loss_reg = criterion_reg(reg_out.squeeze(1), torch.tensor(y_reg)) - loss_cls = criterion_cls(cls_logits, y_cls) - # Weighted multi-task loss - train_loss_val = w_reg * loss_reg + w_cls * loss_cls - train_loss.append(train_loss_val.detach().numpy()) - val_reg_out, val_cls_logits, val_cls_pred = model(torch.tensor(X_valid_feat)) - valid_loss.append(criterion_reg(val_reg_out.detach().squeeze(1), torch.tensor(Y_valid)) * w_reg) - test_reg_out, test_cls_logits, test_cls_pred = model(torch.tensor(X_test_feat)) - test_loss.append(criterion_reg(test_reg_out.detach().squeeze(1), torch.tensor(Y_test)) * w_reg) - mean_loss.append(mean_squared_error(y_reg, [batch_mean for elem in y_reg])) - if epochs>(epoch+1): - train_loss_val.backward() - optimizer.step() - return model, train_loss, valid_loss, test_loss, mean_loss + for batch_idx_clf, (X_batch_clf, y_clf) in enumerate(train_loader_clf): + optimizer.zero_grad() + reg_out, cls_logits, clf_out = model(torch.tensor(X_batch_reg).detach().float(),torch.tensor(X_batch_clf).detach().float()) + loss_reg = criterion_reg(reg_out.squeeze(1), torch.tensor(y_reg))*0.5 + loss_clf= criterion_clf(clf_out.float(), torch.tensor(y_clf).long())*0.5 + train_loss_val = w_reg * loss_reg + w_cls * loss_clf + if epochs>(epoch+1): + loss_reg.backward() + loss_clf.backward() + optimizer.step() + + with torch.no_grad(): + train_loss_lst.append(loss_reg.detach().numpy()) + train_loss_clf_lst.append(loss_clf.detach().numpy()) + val_reg_out, val_cls_logits, val_clf_out=model(torch.tensor(X_valid_feat_reg).float(),torch.tensor(X_valid_feat_clf).float()) + val_loss_reg = criterion_reg(val_reg_out.squeeze(1), torch.tensor(Y_valid_reg)) + val_loss_clf= criterion_clf(val_clf_out.float(), torch.tensor(Y_valid_clf).long()) + valid_loss_lst.append(val_loss_reg) + valid_loss_clf_lst.append(val_loss_clf) + + test_reg_out, test_cls_logits, test_clf_out=model(torch.tensor(X_test_feat_reg).float(),torch.tensor(X_test_feat_clf).float()) + test_loss_reg = criterion_reg(test_reg_out.squeeze(1), torch.tensor(Y_test_reg)) + test_loss_clf= criterion_clf(test_clf_out.float(), torch.tensor(Y_test_clf).long()) + test_loss_lst.append(test_loss_reg) + test_loss_clf_lst.append(test_loss_clf) + + logging.info(f"Epoch={epoch}\t train_loss={train_loss_lst[-1]}||\t val_loss_reg={valid_loss_lst[-1]}\t val_loss_clf={valid_loss_clf_lst[-1]}\t|| test_loss={test_loss_lst[-1]}\t test_loss={test_loss_clf_lst[-1]}") + mean_loss_lst.append(mean_squared_error(y_reg, [batch_mean for elem in y_reg])) + if early_stopper.step(valid_loss_lst[-1]*0.5+valid_loss_clf_lst[-1]*0.5, model): + logging.info("Early stopping triggered.") + early_stopper.restore_best_weights(model) + break + return model,train_loss_lst,train_loss_clf_lst,valid_loss_lst,valid_loss_clf_lst, test_loss_lst,test_loss_clf_lst, mean_loss_lst def train_multihead_v0(model, epochs=100): @@ -339,10 +525,10 @@ def train_multihead_v0(model, epochs=100): # Loss calculations loss_reg = criterion_reg(reg_out, y_reg) loss_cls = criterion_cls(cls_logits, y_cls) - print(f"loss_reg={loss_reg}\tcls_logits={cls_logits}") + logging.info(f"loss_reg={loss_reg}\tcls_logits={cls_logits}") # Weighted multi-task loss loss = w_reg * loss_reg + w_cls * loss_cls - print(loss) + logging.info(loss) loss.backward() optimizer.step() diff --git a/tgrag/experiments/mlp_experiments/mlp_train.py b/tgrag/experiments/mlp_experiments/mlp_train.py deleted file mode 100644 index a9480f13..00000000 --- a/tgrag/experiments/mlp_experiments/mlp_train.py +++ /dev/null @@ -1,408 +0,0 @@ -import argparse -import sklearn -import logging -from typing import Dict, cast -import pickle -import numpy as np -from tgrag.utils.args import parse_args -from tgrag.utils.logger import setup_logging -from tgrag.utils.path import get_root_dir -from tqdm import tqdm -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.neural_network import MLPRegressor as Sklearn_MLPRegressor -from mlp_modules import MLPRegressor -from mlp_modules import MLP3LayersPredictor -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error -from statistics import mean -from mlp_modules import MultiTaskMLP, train_multihead,train_mlp,train_scikitlearn_regressor -from sklearn.preprocessing import normalize -from utils import normalize_embeddings,train_valid_test_split,resize_emb,plot_histogram,plot_loss,plot_regression_scatter,eval - -def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", - month_lst=["dec", "nov", "oct"], target="pc1", agg="avg",normalize=False): - months_emb_lst = [] - if embed_type == "GNN_GAT": - for month in month_lst: - with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: - embd_dict = pickle.load(f) - if normalize: - embd_dict=normalize_embeddings(embd_dict) - months_emb_lst.append(embd_dict) - common_domains_set = set(months_emb_lst[0].keys()).intersection(months_emb_lst[1].keys()).intersection( - months_emb_lst[2].keys()) - - for key in common_domains_set: - for i in range(1, len(months_emb_lst)): - if agg == "concat": - months_emb_lst[0][key].extend(months_emb_lst[i][key]) - elif agg == "min": - months_emb_lst[0][key] = [min(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] - elif agg == "max": - months_emb_lst[0][key] = [max(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] - elif agg == "avg": - months_emb_lst[0][key] = [(a + b) / 2 for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] - - concat_dict = {k: v for k, v in months_emb_lst[0].items() if k in common_domains_set} - # print(f"concat Nmonth emb size={len(concat_dict[list(concat_dict.keys())[0]])}") - # print(f"len of keys={len(concat_dict.keys())}") - return concat_dict - - -def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", - month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): - months_emb_PhishTank_lst = [] - months_emb_URLhaus_lst = [] - months_emb_legit_lst = [] - for month in month_lst: - with open(f'{path}/PhishTank_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: - months_emb_PhishTank_lst.append(pickle.load(f)) - with open(f'{path}/URLHaus_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: - months_emb_URLhaus_lst.append(pickle.load(f)) - with open(f'{path}/IP2Location_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: - months_emb_legit_lst.append(pickle.load(f)) - - for ds_months in [months_emb_PhishTank_lst, months_emb_URLhaus_lst, months_emb_legit_lst]: - for key in ds_months[0].keys(): - for i in range(1, len(ds_months)): # loop on dataset months - if key in ds_months[i]: - if agg == "concat": - ds_months[0][key].extend(ds_months[i][key]) - # print(len(ds_months[0][key])) - elif agg == "min": - ds_months[0][key] = [min(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] - elif agg == "max": - ds_months[0][key] = [max(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] - elif agg == "avg": - ds_months[0][key] = [(a + b) / 2 for a, b in zip(ds_months[0][key], ds_months[i][key])] - # print(len(ds_months[0][key])) - return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] - - -def load_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=256,normalize=False): - if embed_type == "text": - if model_name == "embeddinggemma-300m": - with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingQwen3-0.6B": - with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingQwen3-8B": - with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingTE3L": - with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "IPTC_Topic_emb": - with open(f'IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl','rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "domainName": - with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "GNN_GAT": - # with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "IPTC_Topic": - with open(f'IPTCTopicModeling/dqr_IPTC-news-topic_scores.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "IPTC_Topic_freq": - with open(f'IPTCTopicModeling/dqr_topics_frequancy_norm_dict.pkl','rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "IPTC_Topic_emb": - with open(f'IPTCTopicModeling/dqr_dec_IPTC_predFinalLayer_emb_dict.pkl','rb') as f: - embd_dict = pickle.load(f) - # print(list(embd_dict.keys())[0],embd_dict[list(embd_dict.keys())[0]]) - elif embed_type == "3Feat": - with open(f'/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_3Feat_dict.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "3Feat2": - with open(f'/shared_mnt/github_repos/CrediGraph/data/dqr/dqr_3Feat_dict2.pkl', 'rb') as f: - embd_dict = pickle.load(f) - - elif embed_type == "TFIDF": - # with open(f'{path}/dqr_TFIDF_emb.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - # with open(f'{path}/dqr_TFIDF_emb_8465.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - # with open(f'{path}/dqr_TFIDF_emb_19437.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - if month == "dec": - with open(f'{path}/dqr_dec_TFIDF_weaksupervision_emb_222755.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif month == "nov": - with open(f'{path}/dqr_nov_TFIDF_weaksupervision_emb_258729.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif month == "oct": - with open(f'{path}/dqr_oct_TFIDF_weaksupervision_emb_19085.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "PASTEL": - with open(f'{path}/dqr_pastel_dict.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "PASTEL_hasContent": - with open(f'{path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: - embd_dict = pickle.load(f) - if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) - embd_dict={ k:v[0]['emb'] for k,v in embd_dict.items()} - elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) - embd_dict={ k:v[0][1] for k,v in embd_dict.items()} - if normalize: - embd_dict=normalize_embeddings(embd_dict) - return embd_dict - - -def load_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", - target="pc1", gnn_emb=None, agg=None): - embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = {}, {}, {} - if embed_type == "text": - if model_name == "embeddinggemma-300m": - with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_768.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingQwen3-0.6B": - with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingQwen3-8B": - with open(f'{path}/cc_dec_2024_phishtank_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: - embd_dict_phishtank = pickle.load(f) - with open(f'{path}/cc_dec_2024_URLhaus_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: - embd_dict_URLhaus = pickle.load(f) - with open(f'{path}/cc_dec_2024_PhishDataset_legit_Qwen3-Embedding-8B_4096.pkl', 'rb') as f: - embd_dict_PhishDataset_legit = pickle.load(f) - elif model_name == "embeddingTE3L": - with open(f'{path}/phishtank_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: - embd_dict_phishtank = pickle.load(f) - with open(f'{path}/URLhaus_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: - embd_dict_URLhaus = pickle.load(f) - with open(f'{path}/PhishDataset_legit_{month}_TE3L_weaksupervision_emb_3072.pkl', 'rb') as f: - embd_dict_PhishDataset_legit = pickle.load(f) - - if gnn_emb == True: - print("len of embd_dict_phishtank before appending GNN=", - len(embd_dict_phishtank[list(embd_dict_phishtank.keys())[0]])) - if agg is None: - with open(f'{path}/PhishTank_{target}_rni_embeddings.pkl', 'rb') as f: - gnn_embd_dict_phishtank = pickle.load(f) - with open(f'{path}/URLHaus_{target}_rni_embeddings.pkl', 'rb') as f: - gnn_embd_dict_URLhaus = pickle.load(f) - with open(f'{path}/IP2Location_{target}_rni_embeddings.pkl', 'rb') as f: - gnn_embd_dict_PhishDataset_legit = pickle.load(f) - else: - gnn_embd_dict_phishtank, gnn_embd_dict_URLhaus, gnn_embd_dict_PhishDataset_legit = load_agg_Nmonth_weaksupervision_emb_dict( - embed_type, path, model_name, month_lst=["dec", "nov", "oct"], target=target, agg=agg) - - ############# Append Emb ############# - for k in embd_dict_phishtank: - embd_dict_phishtank[k] = gnn_embd_dict_phishtank[k] + embd_dict_phishtank[k] - for k in embd_dict_URLhaus: - embd_dict_URLhaus[k] = gnn_embd_dict_URLhaus[k] + embd_dict_URLhaus[k] - for k in embd_dict_PhishDataset_legit: - embd_dict_PhishDataset_legit[k] = gnn_embd_dict_PhishDataset_legit[k] + embd_dict_PhishDataset_legit[k] - print("len of embd_dict_phishtank after appending GNN=", - len(embd_dict_phishtank[list(embd_dict_phishtank.keys())[0]])) - - - elif embed_type == "domainName": - with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "GNN_GAT": - with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "TFIDF": - # with open(f'{path}/dqr_TFIDF_emb.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - # with open(f'{path}/dqr_TFIDF_emb_8465.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - # with open(f'{path}/dqr_dec_TFIDF_emb_19437.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - # with open(f'{path}/dqr_TFIDF_emb_19437.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - emb_size = "222755" if month == "dec" else "258729" if month == "nov" else "19085" - with open(f'{path}/phishtank_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: - embd_dict_phishtank = pickle.load(f) - with open(f'{path}/URLhaus_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: - embd_dict_URLhaus = pickle.load(f) - with open(f'{path}/phishDataset_legit_{month}_TFIDF_weaksupervision_emb_{emb_size}.pkl', 'rb') as f: - embd_dict_PhishDataset_legit = pickle.load(f) - - return embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit - -def mlp_train(args) -> None: - ############## Load training data and split ############### - text_emb_dict = load_emb_dict(args.embed_type, args.text_emb_path, args.emb_model, args.month,normalize=False) - labeled_11k_df = pd.read_csv(f"{args.dqr_path}/domain_ratings.csv") - targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") - targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) - labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(text_emb_dict)] - labeled_11k_df = labeled_11k_df.reset_index(drop=True) - ############### filter by the 8K GNN Nodes ################### - if args.filter_by_GNN_nodes: - labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(targets_nodes_df["domain_rev"])] - labeled_11k_df = labeled_11k_df.reset_index(drop=True) - if args.filter_by_PASTEL_domains: - pastel_emb_dict = {} - with open(f'{args.dqr_path}/dqr_hasContent_pastel_dict.pkl', 'rb') as f: - pastel_emb_dict = pickle.load(f) - labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(pastel_emb_dict.keys())] - labeled_11k_df = labeled_11k_df.reset_index(drop=True) - ################# Train ##################### - results = [] - for i in range(args.runs): - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, labeled_11k_df,key='domain',test_valid_size=args.test_valid_size) - if args.embed_type == "TFIDF": - print(f"TFIDF dim={text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]}") - X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, X_test, - gnn_emb=None, trim_to= - text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]) - else: - if args.use_gnn_emb or args.use_topic_emb: - if args.use_gnn_emb: - if args.agg_month_emb: - gnn_emb_dict = load_agg_Nmonth_emb_dict("GNN_GAT", args.emb_path, agg="avg") - else: - gnn_emb_dict = load_emb_dict("GNN_GAT", args.gnn_emb_path,month=args.month) - labeled_11k_df = labeled_11k_df[labeled_11k_df["domain"].isin(gnn_emb_dict.keys())] - labeled_11k_df = labeled_11k_df.reset_index(drop=True) - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target,labeled_11k_df,key='domain',test_valid_size=args.test_valid_size) - else: - gnn_emb_dict = None - if args.use_topic_emb: - # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_freq", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_emb", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("3Feat", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("3Feat2", args.emb_path) - # gnn_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) - topic_iptc_emb_dict = load_emb_dict("PASTEL_hasContent", args.text_emb_path) - else: - topic_iptc_emb_dict = None - X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, - X_test, gnn_emb=gnn_emb_dict, - topic_emb=topic_iptc_emb_dict, - trim_to=args.emb_dim) - else: - X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, - X_test, gnn_emb=None, topic_emb=None, - trim_to=args.emb_dim) - - print(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") - if args.MultiHead == False: - ###################### PYTorch Regressor ###################### - if args.library=="pytorch": - mlp_reg = MLP3LayersPredictor(len(X_train_feat[0])) - # mlp_reg = MLPRegressor(len(X_train_feat[0]),hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16)) - mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_mlp(mlp_reg, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, epochs=args.epochs) - ######################## Scikit-Learn ################### - elif args.library=="sklearn": - mlp_reg = Sklearn_MLPRegressor(hidden_layer_sizes=(128, 32), - activation='relu', solver='adam',max_iter=args.max_iter, random_state=42, - verbose=False, learning_rate_init=args.lr) - mlp_reg.out_activation_ = 'sigmoid' - mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_scikitlearn_regressor(mlp_reg, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, epochs=args.epochs) - - else: - mlp_reg = MultiTaskMLP(input_dim=len(X_train_feat[0]), num_classes=args.num_classes, - hidden_dims=[int(len(X_train_feat[0]) * 0.9), 64, 16]) - mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_multihead(mlp_reg, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, lr=args.lr, - epochs=args.epochs, w_reg=0.5, - w_cls=0.5, - num_classes=args.num_classes) - ###################### Save Model #################### - run_file_name=f"{args.plots_out_path}/dqr_{args.month}_{args.target}_{args.library}_{args.embed_type}_{args.emb_model}_{'GNN-RNI' if args.use_gnn_emb else ''}_{'topic-emb' if args.use_topic_emb else ''}_run{str(i)}" - with open(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_credibench_MLP_Model.pkl", 'wb') as file: - pickle.dump(mlp_reg, file) - ################## Weak Supervision ########### - if args.generate_weaksupervision_scores == True: - if args.agg_month_emb: - embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = load_weaksupervision_emb_dict( - args.embed_type, args.text_emb_path, args.emb_model, month="dec", target=args.target, - gnn_emb=args.use_gnn_emb, agg="avg") - else: - embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = load_weaksupervision_emb_dict( - args.embed_type, args.text_emb_path, args.emb_model, month="dec", target=args.target, - gnn_emb=args.use_gnn_emb, agg=None) - - phishtank_features = [v for k, v in embd_dict_phishtank.items()] - phishtank_pred = mlp_reg.predict(phishtank_features) - pd.DataFrame(zip(embd_dict_phishtank.keys(), phishtank_pred), - columns=["domain", f"pred_{args.target}"]).to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_Phishtank_pred.csv",index=None) - # print("phishtank_pred=",phishtank_pred) - - URLhaus_features = [v for k, v in embd_dict_URLhaus.items()] - URLhaus_pred = mlp_reg.predict(URLhaus_features) - pd.DataFrame(zip(embd_dict_URLhaus.keys(), URLhaus_pred), columns=["domain", f"pred_{args.target}"]).to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_URLhaus_pred.csv",index=None) - - PhishDataset_legit_features = [v for k, v in embd_dict_PhishDataset_legit.items()] - PhishDataset_legit_pred = mlp_reg.predict(PhishDataset_legit_features) - pd.DataFrame(zip(embd_dict_PhishDataset_legit.keys(), PhishDataset_legit_pred), - columns=["domain", f"pred_{args.target}"]).to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_legit_pred.csv",index=None) - ################## Plot and Eval ############### - true = y_test - pred = mlp_reg.predict(X_test_feat) - - plot_loss(train_loss, valid_loss, test_loss, mean_loss, run_file_name+"_loss.pdf") - plot_histogram(true, pred, run_file_name+"_testset_true_vs_pred_frequancy.pdf") - plot_regression_scatter(true, pred, run_file_name+"_testset_true_vs_pred_scatter.pdf") - MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(pred, true) - min_error_dict["domain"] = X_test.iloc[min_error_dict["test_idx"]]["domain"] - max_error_dict["domain"] = X_test.iloc[max_error_dict["test_idx"]]["domain"] - results.append([MSE, MAE, R2, Mean_MAE, str(min_error_dict), str(max_error_dict), str(args)]) - print( - f"Run{i}:MSE={MSE}\tR2={R2}\tMAE={MAE}\tMean_MAE={Mean_MAE}\tmin_error_dict={min_error_dict}\tmax_error_dict={max_error_dict}") - ############ save test results ############ - X_test.to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_testset.csv",index=None) - pd.DataFrame(zip(true, pred), columns=["true", "pred"]).to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_pred.csv",index=None) - - results_df = pd.DataFrame(results, columns=['MSE', 'MAE', 'R2', 'Mean_MAE', 'Min_AE', 'Max_AE', 'args']) - results_df.to_csv(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_results.csv",index=None) - - for col in ['MSE', 'MAE', 'R2', 'Mean_MAE']: - print(f"{col}: Mean={results_df[col].mean()}\tstd={results_df[col].std()}") - - -if __name__ == '__main__': - root = str(get_root_dir()) - parser = argparse.ArgumentParser(description="MLP Experiments") - parser.add_argument("--target", type=str, default="pc1", choices=["pc1", "mbfc", "mbfc_bias"], help="the credability target") - parser.add_argument("--text_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") - parser.add_argument("--gnn_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") - parser.add_argument("--dqr_path", type=str, default=str(root + "/data/dqr"),help="dqr dataset path") - parser.add_argument("--embed_type", type=str, default="text", - choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") - parser.add_argument("--emb_model", type=str, default="embeddingTE3L", - choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", - "IPTC_Topic_emb"],help="LLM embedding model") - parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") - parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") - parser.add_argument("--emb_dim", type=int, default=4096,help="embedding size") - parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") - parser.add_argument("--lr", type=float, default=5e-3,help="learning rate") - parser.add_argument("--epochs", type=int,default=100, help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L - parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") - parser.add_argument("--runs", type=int, default=1,help="# training runs") - parser.add_argument("--use_gnn_emb", type=bool, default=True,help="append GNN embedding") - parser.add_argument("--agg_month_emb", type=bool, default=False, help="aggregate montly GNN embeddings") - parser.add_argument("--use_topic_emb", type=bool, default=False,help="use topic modeling features") - parser.add_argument("--filter_by_GNN_nodes", type=bool, default=True,help="filter by domains has GNN embeddings") - parser.add_argument("--filter_by_PASTEL_domains", type=bool, default=False,help="filter by domains has PASTEL embeddings") - parser.add_argument("--MultiHead", type=bool, default=False, help="Use MultiHead Model") - parser.add_argument("--num_classes", type=int, default=3,help="# classifcation classes for Multihead model") - parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") - parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") - parser.add_argument("--library", type=str, default="sklearn", choices=["pytorch", "sklearn"],help="ML library to use") - args = parser.parse_args() - print("args=", args) - mlp_train(args) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/mlp_train_classifier.py b/tgrag/experiments/mlp_experiments/mlp_train_classifier.py index 24d451c1..a67a29ef 100644 --- a/tgrag/experiments/mlp_experiments/mlp_train_classifier.py +++ b/tgrag/experiments/mlp_experiments/mlp_train_classifier.py @@ -1,11 +1,6 @@ import argparse -import sklearn -import logging -from typing import Dict, cast import pickle import numpy as np -from tgrag.utils.args import parse_args -from tgrag.utils.logger import setup_logging from tgrag.utils.path import get_root_dir from tqdm import tqdm import pandas as pd @@ -13,303 +8,121 @@ from sklearn.neural_network import MLPClassifier as Sklearn_MLPClassifier from mlp_modules import MLPRegressor from mlp_modules import MLP3LayersPredictor -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error -from statistics import mean -from mlp_modules import MultiTaskMLP, train_multihead,train_mlp,train_scikitlearn_classifier,train_classifier,LabelPredictor,train_classifier_unbalanced +from mlp_modules import MultiTaskMLP,train_scikitlearn_classifier,LabelPredictor,train_classifier_unbalanced from sklearn.preprocessing import normalize -from utils import normalize_embeddings, plot_histogram,train_valid_test_split,resize_emb,\ - plot_histogram,plot_loss,plot_regression_scatter,eval,\ - plot_classesCount +from utils import train_valid_test_split,resize_emb,\ + plot_loss,plot_regression_scatter,eval,\ + plot_classesCount,plot_confusion_matrix from sklearn.metrics import confusion_matrix, accuracy_score from sklearn.metrics import f1_score -from utils import search_parquet_emb,search_parquet_duckdb -def load_agg_Nmonth_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", - month_lst=["dec", "nov", "oct"], target="pc1", agg="avg",normalize=False): - months_emb_lst = [] - if embed_type == "GNN_GAT": - for month in month_lst: - with open(f'{path}/dqr_gnn_embeddings/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: - embd_dict = pickle.load(f) - if normalize: - embd_dict=normalize_embeddings(embd_dict) - months_emb_lst.append(embd_dict) - common_domains_set = set(months_emb_lst[0].keys()).intersection(months_emb_lst[1].keys()).intersection( - months_emb_lst[2].keys()) - - for key in common_domains_set: - for i in range(1, len(months_emb_lst)): - if agg == "concat": - months_emb_lst[0][key].extend(months_emb_lst[i][key]) - elif agg == "min": - months_emb_lst[0][key] = [min(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] - elif agg == "max": - months_emb_lst[0][key] = [max(a, b) for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] - elif agg == "avg": - months_emb_lst[0][key] = [(a + b) / 2 for a, b in zip(months_emb_lst[0][key], months_emb_lst[i][key])] - - concat_dict = {k: v for k, v in months_emb_lst[0].items() if k in common_domains_set} - # print(f"concat Nmonth emb size={len(concat_dict[list(concat_dict.keys())[0]])}") - # print(f"len of keys={len(concat_dict.keys())}") - return concat_dict - - -def load_agg_Nmonth_weaksupervision_emb_dict(embed_type, path="../../../data", model_name="embeddinggemma-300m", - month_lst=["dec", "nov", "oct"], target="pc1", agg="avg"): - months_emb_PhishTank_lst = [] - months_emb_URLhaus_lst = [] - months_emb_legit_lst = [] - for month in month_lst: - with open(f'{path}/PhishTank_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: - months_emb_PhishTank_lst.append(pickle.load(f)) - with open(f'{path}/URLHaus_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: - months_emb_URLhaus_lst.append(pickle.load(f)) - with open(f'{path}/IP2Location_{target}_rni_{month}_2024_embeddings.pkl', 'rb') as f: - months_emb_legit_lst.append(pickle.load(f)) - - for ds_months in [months_emb_PhishTank_lst, months_emb_URLhaus_lst, months_emb_legit_lst]: - for key in ds_months[0].keys(): - for i in range(1, len(ds_months)): # loop on dataset months - if key in ds_months[i]: - if agg == "concat": - ds_months[0][key].extend(ds_months[i][key]) - # print(len(ds_months[0][key])) - elif agg == "min": - ds_months[0][key] = [min(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] - elif agg == "max": - ds_months[0][key] = [max(a, b) for a, b in zip(ds_months[0][key], ds_months[i][key])] - elif agg == "avg": - ds_months[0][key] = [(a + b) / 2 for a, b in zip(ds_months[0][key], ds_months[i][key])] - # print(len(ds_months[0][key])) - return months_emb_PhishTank_lst[0], months_emb_URLhaus_lst[0], months_emb_legit_lst[0] - - -def load_emb_dict(embed_type, path="../../../data",pickle_name=None, model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=8192,normalize=False): - if pickle_name: - with open(f'{path}/{pickle_name}', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "text": - if model_name == "embeddinggemma-300m": - with open(f'{path}/dqr_{month}_text_embeddinggemma-300m_{emb_dim}.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingQwen3-0.6B": - with open(f'{path}/dqr_{month}_text_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingQwen3-8B": - with open(f'{path}/dqr_{month}_text_embeddingQwen3-8B_4096.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif model_name == "embeddingTE3L": - with open(f'{path}/dqr_{month}_text_embeddingTE3L_3072.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "domainName": - with open(f'{path}/dqr_domainName_embeddingQwen3-0.6B_1024.pkl', 'rb') as f: - embd_dict = pickle.load(f) - elif embed_type == "GNN_GAT": - # with open(f'{path}/11Kdataset_GAT_targets_connected_edges_GNN_textE_300E_pc1_emb.pkl', 'rb') as f: - # embd_dict=pickle.load(f) - with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: - embd_dict = pickle.load(f) - - if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) - embd_dict={ k:v[0]['emb'][0:emb_dim] for k,v in embd_dict.items()} - elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) - embd_dict={ k:v[0][1][0:emb_dim] for k,v in embd_dict.items()} - if normalize: - embd_dict=normalize_embeddings(embd_dict) - return embd_dict - -def load_emb_dict_from_parquet(embed_type, path="../../../data", model_name="embeddinggemma-300m", month="dec", target="pc1", emb_dim=8192,normalize=False): - embd_dict=None - if embed_type == "text": - if model_name == "embeddinggemma-300m": - # embd_dict=search_parquet_emb(path,f'weaksupervision_content_emb_{month}2024.parquet', column_name="domain",search_values=None,schema={'key':'domain','val':'embeddings'}) - embd_dict=search_parquet_duckdb(f'{path}/weaksupervision_content_emb_{month}2024.parquet', col="domain",q_domains=None,max_memory="8GB",schema={'key':'domain','val':'embeddings'}) - elif embed_type == "GNN_GAT": - with open(f'{path}/{month}_{target}_dqr_domain_rni_embeddings.pkl', 'rb') as f: - embd_dict = pickle.load(f) - - if isinstance(embd_dict[list(embd_dict.keys())[0]][0], dict): #list of dicts per domain pages (parquet format) - embd_dict={ k:v[0]['emb'][0:emb_dim] for k,v in embd_dict.items()} - elif isinstance(embd_dict[list(embd_dict.keys())[0]][0], list) and isinstance(embd_dict[list(embd_dict.keys())[0]][0][0], str) : #list of lists per domain pages (parquet format) - embd_dict={ k:v[0][1][0:emb_dim] for k,v in embd_dict.items()} - if normalize: - embd_dict=normalize_embeddings(embd_dict) - return embd_dict - - +from dataset_loader import DomainRel +import logging +from tgrag.utils.logger import setup_logging +def write_testset_emb(args,X_test,X_test_feat): + test_set_emb_dict=dict(zip(X_test["domain"].tolist(),X_test_feat)) + test_emb_file_name=f"{args.plots_out_path}/weaksupervision_{args.month}_{args.embed_type}_{args.emb_model}_{f'GAT-{args.gnn_encoder}' if args.use_gnn_emb else ''}{'-'+args.agg_function+'_' if args.agg_month_emb else ''}test_set_emb_dict.pkl" + with open(test_emb_file_name, 'wb') as file: + pickle.dump(test_set_emb_dict, file) def mlp_classifier(args) -> None: - ############## Load training data and split ############### - full_emb_dict = load_emb_dict(args.embed_type, args.text_emb_path, pickle_name="weak_content_emb_embeddinggemma-300m_768.pkl",emb_dim=256,normalize=False) - month_emb_dict = load_emb_dict_from_parquet(args.embed_type, args.text_emb_path, args.emb_model, args.month,normalize=False) - weaklabeles_df = pd.read_csv(f"{args.weaksupervision_path}/weaklabels.csv") - # targets_nodes_df = pd.read_csv(f"{args.dqr_path}/targets_nodes_df.csv") - # targets_nodes_df["domain_rev"] = targets_nodes_df["domain"].apply(lambda x: '.'.join(str(x).split('.')[::-1])) - weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(full_emb_dict)] - weaklabeles_df = weaklabeles_df.reset_index(drop=True) - text_emb_dict={} - text_emb_dict.update(month_emb_dict) - text_emb_dict.update({k:v for k,v in full_emb_dict.items() if k not in month_emb_dict}) - ############### filter by the GNN graph node splits ################### - if args.filter_by_GNN_nodes: - test_domains_df=search_parquet_duckdb(f'{args.weaksupervision_path}/splits/{args.month}2024/test_split.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) - test_domains_df['domain']=test_domains_df['Domains_test'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) - print(f"test set lables count ={weaklabeles_df[weaklabeles_df["domain"].isin(test_domains_df['domain'])]["weak_label"].value_counts()}") - valid_domains_df=search_parquet_duckdb(f'{args.weaksupervision_path}/splits/{args.month}2024/valid_split.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) - valid_domains_df['domain']=valid_domains_df['Domains_valid'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) - print(f"valid set lables count ={weaklabeles_df[weaklabeles_df["domain"].isin(valid_domains_df['domain'])]["weak_label"].value_counts()}") - train_domains_df=search_parquet_duckdb(f'{args.weaksupervision_path}/splits/{args.month}2024/train_split.parquet', col=None,q_domains=None,max_memory="8GB",schema=None) - train_domains_df['domain']=train_domains_df['Domains_train'].apply(lambda x: '.'.join(str(x).split('.')[::-1])) - print(f"train set lables count ={weaklabeles_df[weaklabeles_df["domain"].isin(train_domains_df['domain'])]["weak_label"].value_counts()}") - test_domains_set=set(test_domains_df['domain']) - valid_domains_set=set(valid_domains_df['domain']) - train_domains_set=set(train_domains_df['domain']) - filter_by_domains_set=test_domains_set.union(valid_domains_set).union(train_domains_set) - weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(filter_by_domains_set)] - weaklabeles_df = weaklabeles_df.reset_index(drop=True) - text_emb_dict={k:v for k,v in text_emb_dict.items() if k in filter_by_domains_set} + run_file_name=f"DomainRel_{args.month}_{args.domainRel_target}_{args.library}_{args.embed_type}_{args.emb_model}_{f'GAT-{args.gnn_encoder}' if args.use_gnn_emb else ''}{'-'+args.agg_function if args.agg_month_emb else ''}{'_topic-emb' if args.use_topic_emb else ''}" + setup_logging(f"{args.logs_out_path}/{run_file_name}.log") + logging.info(f"args={args}") + run_file_name=f"{args.plots_out_path}/{run_file_name}" + ############################ + X_train, y_train, X_valid, y_valid, X_test, y_test,X_train_feat, X_valid_feat, X_test_feat=DomainRel.load_run_embeddings(args) + ############## Save Test set Embeddings dict pickle ############# + write_testset_emb(args,X_test,X_test_feat) ################# Train ##################### results = [] - for i in range(args.runs): - if args.filter_by_GNN_nodes: - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, weaklabeles_df,key='domain',test_valid_size=args.test_valid_size,regressor=False,train_lst=train_domains_set,valid_lst=valid_domains_set,test_lst=test_domains_set) - else: - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target, weaklabeles_df,key='domain',test_valid_size=args.test_valid_size,regressor=False) - ############### Resize embeddings ################## - if args.embed_type == "TFIDF": - print(f"TFIDF dim={text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]}") - X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, X_test, - gnn_emb=None, trim_to= - text_emb_dict[list(text_emb_dict.keys())[0]].shape[0]) - else: - if args.use_gnn_emb or args.use_topic_emb: - if args.use_gnn_emb: - if args.agg_month_emb: - gnn_emb_dict = load_agg_Nmonth_emb_dict("GNN_GAT", args.emb_path, agg="avg") - else: - gnn_emb_dict = load_emb_dict("GNN_GAT", args.gnn_emb_path,month=args.month) - weaklabeles_df = weaklabeles_df[weaklabeles_df["domain"].isin(gnn_emb_dict.keys())] - weaklabeles_df = weaklabeles_df.reset_index(drop=True) - X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(args.target,weaklabeles_df,args.test_valid_size,regressor=False) - else: - gnn_emb_dict = None - if args.use_topic_emb: - # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_freq", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("IPTC_Topic_emb", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("3Feat", args.emb_path) - # topic_iptc_emb_dict = load_emb_dict("3Feat2", args.emb_path) - # gnn_emb_dict = load_emb_dict("IPTC_Topic", args.emb_path) - topic_iptc_emb_dict = load_emb_dict("PASTEL_hasContent", args.text_emb_path) - else: - topic_iptc_emb_dict = None - X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, - X_test, gnn_emb=gnn_emb_dict, - topic_emb=topic_iptc_emb_dict, - trim_to=args.emb_dim) - else: - X_train_feat, X_valid_feat, X_test_feat = resize_emb(text_emb_dict, args.target, X_train, X_valid, - X_test, gnn_emb=None, topic_emb=None, - trim_to=args.emb_dim) - - print(f"X_train_feat.shape={len(X_train_feat[0]) if type(X_train_feat[0]) == list else X_train_feat[0].shape}") - if args.MultiHead == False: - ###################### PYTorch Regressor ###################### - if args.library=="pytorch": - mlp_clf = LabelPredictor(len(X_train_feat[0])) - # mlp_clf = MLPRegressor(len(X_train_feat[0]),hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16)) - mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_classifier_unbalanced(mlp_clf, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, epochs=args.epochs) - - # mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_classifier(mlp_clf, X_train_feat, y_train, - # X_valid_feat, y_valid, X_test_feat, - # y_test, epochs=args.epochs) - ######################## Scikit-Learn ################### - elif args.library=="sklearn": - mlp_clf = Sklearn_MLPClassifier(hidden_layer_sizes=(128, 32), - activation='relu', solver='adam',max_iter=args.max_iter, random_state=42, - verbose=False, learning_rate_init=args.lr,warm_start=True) - mlp_clf.out_activation_ = 'sigmoid' - mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_scikitlearn_classifier(mlp_clf, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, epochs=args.epochs) + + for i in range(args.runs): + logging.info(f"#################### Run {i} ##################") + run_file_name+=f"_run{str(i)}" + ###################### PYTorch Regressor ###################### + if args.library=="pytorch": + mlp_clf = LabelPredictor(len(X_train_feat[0])) + # mlp_clf = MLPRegressor(len(X_train_feat[0]),hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16)) + mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_classifier_unbalanced(mlp_clf, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + + # mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_classifier(mlp_clf, X_train_feat, y_train, + # X_valid_feat, y_valid, X_test_feat, + # y_test, epochs=args.epochs) + ######################## Scikit-Learn ################### + elif args.library=="sklearn": + mlp_clf = Sklearn_MLPClassifier(hidden_layer_sizes=(128, 32), + activation='relu', solver='adam',max_iter=args.max_iter, random_state=42, + verbose=False, learning_rate_init=args.lr,warm_start=True) + mlp_clf.out_activation_ = 'sigmoid' + mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_scikitlearn_classifier(mlp_clf, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) - else: - mlp_clf = MultiTaskMLP(input_dim=len(X_train_feat[0]), num_classes=args.num_classes, - hidden_dims=[int(len(X_train_feat[0]) * 0.9), 64, 16]) - mlp_clf, train_loss, valid_loss, test_loss, mean_loss = train_multihead(mlp_clf, X_train_feat, y_train, - X_valid_feat, y_valid, X_test_feat, - y_test, lr=args.lr, - epochs=args.epochs, w_reg=0.5, - w_cls=0.5, - num_classes=args.num_classes) + ###################### Save Model #################### - run_file_name=f"{args.plots_out_path}/weaksupervision_{args.month}_{args.target}_{args.library}_{args.embed_type}_{args.emb_model}_{'GNN-RNI' if args.use_gnn_emb else ''}_{'topic-emb' if args.use_topic_emb else ''}_run{str(i)}" + run_file_name=f"{args.plots_out_path}/weaksupervision_{args.month}_{args.domainRel_target}_{args.library}_{args.embed_type}_{args.emb_model}_{f'GAT-{args.gnn_encoder}' if args.use_gnn_emb else ''}{'-'+args.agg_function if args.agg_month_emb else ''}{'_topic-emb' if args.use_topic_emb else ''}_run{str(i)}" with open(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_credibench_MLP_Model.pkl", 'wb') as file: pickle.dump(mlp_clf, file) ################## Plot and Eval ############### + acc_lst,f1_lst=[],[] true = y_test pred = mlp_clf.predict(X_test_feat) pred=pred.round() accuracy = accuracy_score(true, pred) - print(f"Accuracy: {accuracy:.4f}") + acc_lst.append(accuracy) + logging.info(f"Accuracy: {accuracy:.4f}") cm = confusion_matrix(true, pred) - print(f"cm: {cm}") + logging.info(f"cm: {cm}") f1 = f1_score(true,pred, average='macro') - print(f"F1 score (macro): {f1:.4f}") - + f1_lst.append(f1) + logging.info(f"F1 score (macro): {f1:.4f}") plot_loss(train_loss, valid_loss, test_loss, mean_loss, run_file_name+"_loss.pdf",ylabel="CrossEntropy Loss") plot_classesCount(cm,run_file_name+"_class_frequancy.pdf") plot_regression_scatter(true, pred, run_file_name+"_testset_true_vs_pred_scatter.pdf") - MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(pred, true) - min_error_dict["domain"] = X_test.iloc[min_error_dict["test_idx"]]["domain"] - max_error_dict["domain"] = X_test.iloc[max_error_dict["test_idx"]]["domain"] - # results.append([MSE, MAE, R2, Mean_MAE, str(min_error_dict), str(max_error_dict), str(args)]) - # print(f"Run{i}:MSE={MSE}\tR2={R2}\tMAE={MAE}\tMean_MAE={Mean_MAE}\tmin_error_dict={min_error_dict}\tmax_error_dict={max_error_dict}") - ############ save test results ############ - X_test.to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_testset.csv",index=None) - pd.DataFrame(zip(true, pred), columns=["true", "pred"]).to_csv( - f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_pred.csv",index=None) + plot_confusion_matrix(cm, run_file_name+"_testset_confusion_matrix.pdf") - results_df = pd.DataFrame(results, columns=['MSE', 'MAE', 'R2', 'Mean_MAE', 'Min_AE', 'Max_AE', 'args']) + logging.info(f"ACC MAEN={np.mean(acc_lst)}") + logging.info(f"ACC STD={np.std(acc_lst)}") + results_df = pd.DataFrame(list(zip(acc_lst,f1_lst,[args,args,args])), columns=['ACC', 'F1', 'args']) results_df.to_csv(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_results.csv",index=None) - # for col in ['MSE', 'MAE', 'R2', 'Mean_MAE']: - # print(f"{col}: Mean={results_df[col].mean()}\tstd={results_df[col].std()}") + if __name__ == '__main__': root = str(get_root_dir()) parser = argparse.ArgumentParser(description="MLP Experiments") - parser.add_argument("--target", type=str, default="weak_label", choices=["weak_label"], help="the credability target") - parser.add_argument("--text_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") - parser.add_argument("--gnn_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") - parser.add_argument("--weaksupervision_path", type=str, default=str(root + "/data/weaksupervision"),help="dqr dataset path") + parser.add_argument("--domainRel_target", type=str, default="weak_label", choices=["weak_label"], help="the credability target") + parser.add_argument("--domainRel_text_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") + parser.add_argument("--domainRel_gnn_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") + parser.add_argument("--domainRel_path", type=str, default=str(root + "/data/weaksupervision"),help="dqr dataset path") parser.add_argument("--embed_type", type=str, default="text", choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") - parser.add_argument("--emb_model", type=str, default="embeddinggemma-300m", - choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", + parser.add_argument("--emb_model", type=str, default="Qwen3-Embedding-0.6B", + choices=["Qwen3-Embedding-8B", "Qwen3-Embedding-0.6B", "embeddinggemma-300m", "TE3L","Qwen3-Embedding-8B-Q5_K_M", "IPTC_Topic_emb"],help="LLM embedding model") - parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") + parser.add_argument("--batch_size", type=int, default=4000,help="training batch size") parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") - parser.add_argument("--emb_dim", type=int, default=256,help="embedding size") + parser.add_argument("--emb_dim", type=int, default=1024,help="embedding size") + parser.add_argument("--original_emb_dim", type=int, default=1024,help="The original embedding model dim size") parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") - parser.add_argument("--lr", type=float, default=1e-2,help="learning rate") - parser.add_argument("--epochs", type=int,default=200, help="# training epochs") # default 20, 10 for Qwen38B, 10 for TFIDF-PC1, 4 for TFIDF-mbfc , 8 for TE3L, 7 for GNN+TE3L + parser.add_argument("--lr", type=float, default=1e-1,help="learning rate") + parser.add_argument("--epochs", type=int,default=200, help="# training epochs") parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") + parser.add_argument("--logs_out_path", type=str, default=str(root + "/logs"),help="logging path") parser.add_argument("--runs", type=int, default=1,help="# training runs") - parser.add_argument("--use_gnn_emb", type=bool, default=False,help="append GNN embedding") - parser.add_argument("--agg_month_emb", type=bool, default=False, help="aggregate montly GNN embeddings") - parser.add_argument("--use_topic_emb", type=bool, default=False,help="use topic modeling features") - parser.add_argument("--filter_by_GNN_nodes", type=bool, default=True,help="filter by domains has GNN embeddings") - parser.add_argument("--MultiHead", type=bool, default=False, help="Use MultiHead Model") + parser.add_argument("--use_gnn_emb", action='store_false',help="append GNN embedding") + parser.add_argument("--gnn_encoder", default="text", choices=["RNI","text"],help="append GNN node embedding intialization") + parser.add_argument("--agg_month_emb", action='store_false', help="aggregate montly GNN embeddings") + parser.add_argument("--agg_text_emb", action='store_false', help="aggregate montly text embeddings") + parser.add_argument("--agg_function", type=str, default="avg",choices=["avg","cat"], help="aggregate function") + parser.add_argument("--use_topic_emb", action='store_true',help="use topic modeling features") + parser.add_argument("--filter_by_GNN_nodes", action='store_false',help="filter by domains has GNN embeddings") parser.add_argument("--num_classes", type=int, default=2,help="# classifcation classes for Multihead model") parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") - parser.add_argument("--month", type=str, default="nov", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") parser.add_argument("--library", type=str, default="pytorch", choices=["pytorch", "sklearn"],help="ML library to use") args = parser.parse_args() - print("args=", args) mlp_classifier(args) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/mlp_train_multihead.py b/tgrag/experiments/mlp_experiments/mlp_train_multihead.py new file mode 100644 index 00000000..0172af51 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/mlp_train_multihead.py @@ -0,0 +1,92 @@ +import argparse +import pickle +import logging +from tgrag.utils.path import get_root_dir +from mlp_modules import MultiTaskMLP, train_multihead_unbalanced +from utils import plot_loss,plot_regression_scatter,eval,plot_classesCount,plot_confusion_matrix +from sklearn.metrics import confusion_matrix, accuracy_score +from sklearn.metrics import f1_score +from dataset_loader import DQR,DomainRel +import sys +from tgrag.utils.logger import setup_logging +def mlp_train_multihead(args) -> None: + learnable_loss_weight=True + run_file_name=f"multihead_{args.month}_{args.dqr_target}_{args.library}_{args.embed_type}_{args.emb_model}_{f'GAT-{args.gnn_encoder}' if args.use_gnn_emb else ''}{'-'+args.agg_function if args.agg_month_emb else ''}{'_LLW' if learnable_loss_weight else 'MLW'}" + setup_logging(f"{args.logs_out_path}/{run_file_name}.log",logging.INFO,logging.INFO) + logging.info(f"args={args}") + # setup_logging(f"{run_file_name}.log",logging.INFO,logging.INFO) + run_file_name=f"{args.plots_out_path}/{run_file_name}" + + ############## Load training data and split ############### + reg_X_train, reg_y_train, reg_X_valid, reg_y_valid, reg_X_test, reg_y_test,reg_X_train_feat, reg_X_valid_feat, reg_X_test_feat=DQR.load_run_embeddings(args) + clf_X_train, clf_y_train, clf_X_valid, clf_y_valid, clf_X_test, clf_y_test,clf_X_train_feat, clf_X_valid_feat, clf_X_test_feat=DomainRel.load_run_embeddings(args) + ################# Train ##################### + for i in range(args.runs): + logging.info(f"#################### Run {i} ##################") + run_file_name+=f"_run{str(i)}" + mlp_MT = MultiTaskMLP(input_dim=len(reg_X_train_feat[0]), num_classes=len(set(clf_y_train)), + hidden_dims=[len(clf_X_train_feat[0]),len(clf_X_train_feat[0])//4]) + mlp_MT, train_loss,train_loss_clf, valid_loss,valid_loss_clf, test_loss,test_loss_clf, mean_loss = train_multihead_unbalanced(mlp_MT, reg_X_train_feat, reg_y_train,reg_X_valid_feat, reg_y_valid, reg_X_test_feat,reg_y_test, + clf_X_train_feat, clf_y_train,clf_X_valid_feat, clf_y_valid, clf_X_test_feat,clf_y_test, + lr=args.lr,epochs=args.epochs,batch_size=args.batch_size,use_URL_Loss= learnable_loss_weight) + ###################### Save Model #################### + with open(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_credibench_MLP_Model.pkl", 'wb') as file: + pickle.dump(mlp_MT, file) + ################## Plot and Eval ############### + reg_true = reg_y_test + mlp_MT.eval() + reg_pred = mlp_MT.predict(reg_X_test_feat,task="reg").detach() + clf_pred = mlp_MT.predict(clf_X_test_feat,task="cls").detach() + MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(reg_pred, reg_true) + logging.info(f"Regression: MSE={MSE}\tMAE={MAE}\tR2={R2}\tMean_MAE={Mean_MAE}" ) + clf_pred=clf_pred.round() + clf_true = clf_y_test + accuracy = accuracy_score(clf_true, clf_pred) + logging.info(f"Binary DS: Accuracy= {accuracy:.4f}") + cm = confusion_matrix(clf_true, clf_pred) + logging.info(f"Binary DS:CM= {cm}") + f1 = f1_score(clf_true,clf_pred, average='macro') + logging.info(f"Binary DS F1 score (macro): {f1:.4f}") + plot_loss(train_loss, valid_loss, test_loss, mean_loss, run_file_name+"_multihead_reg_loss.pdf",ylabel="MSE") + plot_loss(train_loss_clf, valid_loss_clf, test_loss_clf, None, run_file_name+"_multihead_clf_loss.pdf",ylabel="CrossEntropy Loss") + plot_classesCount(cm,run_file_name+"_class_frequancy.pdf") + plot_regression_scatter(clf_true, clf_pred, run_file_name+"_testset_multihead_true_vs_pred_scatter.pdf") + plot_confusion_matrix(cm, run_file_name+"_testset_multihead_confusion_matrix.pdf") + +if __name__ == '__main__': + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP Experiments") + parser.add_argument("--domainRel_target", type=str, default="weak_label", choices=["weak_label"], help="the credability target") + parser.add_argument("--dqr_target", type=str, default="pc1", choices=["pc1","mbfc"], help="the credability target") + parser.add_argument("--domainRel_text_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") + parser.add_argument("--domainRel_gnn_emb_path", type=str, default=str(root + "/data/weaksupervision") ,help="emb files path") + parser.add_argument("--domainRel_path", type=str, default=str(root + "/data/weaksupervision"),help="dqr dataset path") + parser.add_argument("--dqr_text_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--dqr_gnn_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--dqr_path", type=str, default=str(root + "/data/dqr"),help="dqr dataset path") + parser.add_argument("--embed_type", type=str, default="text", + choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") + parser.add_argument("--emb_model", type=str, default="embeddingTE3L", + choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", + "IPTC_Topic_emb"],help="LLM embedding model") + parser.add_argument("--batch_size", type=int, default=4000,help="training batch size") + parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") + parser.add_argument("--emb_dim", type=int, default=256,help="embedding size") + parser.add_argument("--original_emb_dim", type=int, default=1024,help="The original embedding model dim size") + parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") + parser.add_argument("--lr", type=float, default=1e-4,help="learning rate") + parser.add_argument("--epochs", type=int,default=200, help="# training epochs") + parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") + parser.add_argument("--logs_out_path", type=str, default=str(root + "/logs"),help="logging path") + parser.add_argument("--runs", type=int, default=1,help="# training runs") + parser.add_argument("--gnn_encoder", default="text", choices=["RNI","text"],help="append GNN node embedding intialization") + parser.add_argument("--use_gnn_emb", action='store_false',help="append GNN embedding") + parser.add_argument("--agg_text_emb", action='store_false', help="aggregate montly text embeddings") + parser.add_argument("--agg_month_emb",action='store_false', help="aggregate montly GNN embeddings") + parser.add_argument("--agg_function", type=str, default="avg",choices=["avg","cat"], help="aggregate function") + parser.add_argument("--filter_by_GNN_nodes", action='store_false',help="filter by domains has GNN embeddings") + parser.add_argument("--num_classes", type=int, default=2,help="# classifcation classes for Multihead model") + parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--library", type=str, default="pytorch", choices=["pytorch", "sklearn"],help="ML library to use") + args = parser.parse_args() + mlp_train_multihead(args) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/mlp_train_regressor.py b/tgrag/experiments/mlp_experiments/mlp_train_regressor.py new file mode 100644 index 00000000..b0dffef9 --- /dev/null +++ b/tgrag/experiments/mlp_experiments/mlp_train_regressor.py @@ -0,0 +1,133 @@ +import argparse +import pickle +from tgrag.utils.path import get_root_dir +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.neural_network import MLPRegressor as Sklearn_MLPRegressor +from mlp_modules import MLPRegressor +from mlp_modules import MLP3LayersPredictor +from mlp_modules import MultiTaskMLP,train_mlp,train_scikitlearn_regressor +from utils import plot_histogram,plot_loss,plot_regression_scatter,eval +from dataset_loader import DQR +from tgrag.utils.logger import setup_logging +import logging +def mlp_train(args) -> None: + run_file_name=f"dqr_{args.month}_{args.dqr_target}_{args.library}_{args.embed_type}_{args.emb_model}_{f'GAT-{args.gnn_encoder}' if args.use_gnn_emb else ''}{'-'+args.agg_function if args.agg_month_emb else ''}{'_topic-emb' if args.use_topic_emb else ''}" + setup_logging(f"{args.logs_out_path}/{run_file_name}.log") + logging.info("args=", args) + run_file_name=f"{args.plots_out_path}/{run_file_name}" + ############################################ + X_train, y_train, X_valid, y_valid, X_test, y_test,X_train_feat, X_valid_feat, X_test_feat=DQR.load_run_embeddings(args) + results = [] + for i in range(args.runs): + logging.info(f"#################### Run {i} ##################") + run_file_name+=f"_run{str(i)}" + + ###################### PYTorch Regressor ###################### + if args.library=="pytorch": + mlp_reg = MLP3LayersPredictor(len(X_train_feat[0])) + # mlp_reg = MLPRegressor(len(X_train_feat[0]),hidden_layer_sizes=(int(len(X_train_feat[0]) * 0.75), 128, 64, 16)) + mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_mlp(mlp_reg, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + ######################## Scikit-Learn ################### + elif args.library=="sklearn": + mlp_reg = Sklearn_MLPRegressor(hidden_layer_sizes=(128, 32), + activation='relu', solver='adam',max_iter=args.max_iter, random_state=42, + verbose=False, learning_rate_init=args.lr) + mlp_reg.out_activation_ = 'sigmoid' + mlp_reg, train_loss, valid_loss, test_loss, mean_loss = train_scikitlearn_regressor(mlp_reg, X_train_feat, y_train, + X_valid_feat, y_valid, X_test_feat, + y_test, epochs=args.epochs) + ###################### Save Model #################### + with open(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_credibench_MLP_Model.pkl", 'wb') as file: + pickle.dump(mlp_reg, file) + ################## Weak Supervision ########### + if args.generate_weaksupervision_scores == True: + if args.agg_month_emb: + embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = DQR.load_weaksupervision_emb_dict( + args.embed_type, args.text_emb_path, args.emb_model, month="dec", target=args.dqr_target, + gnn_emb=args.use_gnn_emb, agg="avg") + else: + embd_dict_phishtank, embd_dict_URLhaus, embd_dict_PhishDataset_legit = DQR.load_weaksupervision_emb_dict( + args.embed_type, args.text_emb_path, args.emb_model, month="dec", target=args.dqr_target, + gnn_emb=args.use_gnn_emb, agg=None) + + phishtank_features = [v for k, v in embd_dict_phishtank.items()] + phishtank_pred = mlp_reg.predict(phishtank_features) + pd.DataFrame(zip(embd_dict_phishtank.keys(), phishtank_pred), + columns=["domain", f"pred_{args.dqr_target}"]).to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_Phishtank_pred.csv",index=None) + # logging.info("phishtank_pred=",phishtank_pred) + + URLhaus_features = [v for k, v in embd_dict_URLhaus.items()] + URLhaus_pred = mlp_reg.predict(URLhaus_features) + pd.DataFrame(zip(embd_dict_URLhaus.keys(), URLhaus_pred), columns=["domain", f"pred_{args.dqr_target}"]).to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_URLhaus_pred.csv",index=None) + + PhishDataset_legit_features = [v for k, v in embd_dict_PhishDataset_legit.items()] + PhishDataset_legit_pred = mlp_reg.predict(PhishDataset_legit_features) + pd.DataFrame(zip(embd_dict_PhishDataset_legit.keys(), PhishDataset_legit_pred), + columns=["domain", f"pred_{args.dqr_target}"]).to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_legit_pred.csv",index=None) + ################## Plot and Eval ############### + true = y_test + pred = mlp_reg.predict(X_test_feat) + + plot_loss(train_loss, valid_loss, test_loss, mean_loss, run_file_name+"_loss.pdf") + plot_histogram(true, pred, run_file_name+"_testset_true_vs_pred_frequency.pdf") + plot_regression_scatter(true, pred, run_file_name+"_testset_true_vs_pred_scatter.pdf") + MSE, MAE, R2, Mean_MAE, min_error_dict, max_error_dict = eval(pred, true) + min_error_dict["domain"] = X_test.iloc[min_error_dict["test_idx"]]["domain"] + max_error_dict["domain"] = X_test.iloc[max_error_dict["test_idx"]]["domain"] + results.append([MSE, MAE, R2, Mean_MAE, str(min_error_dict), str(max_error_dict), str(args)]) + logging.info( + f"Run{i}:MSE={MSE}\tR2={R2}\tMAE={MAE}\tMean_MAE={Mean_MAE}\tmin_error_dict={min_error_dict}\tmax_error_dict={max_error_dict}") + ############ save test results ############ + X_test.to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_testset.csv",index=None) + pd.DataFrame(zip(true, pred), columns=["true", "pred"]).to_csv( + f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_pred.csv",index=None) + + results_df = pd.DataFrame(results, columns=['MSE', 'MAE', 'R2', 'Mean_MAE', 'Min_AE', 'Max_AE', 'args']) + results_df.to_csv(f"{run_file_name}{'_agg' if args.agg_month_emb else ''}_dqr_results.csv",index=None) + + for col in ['MSE', 'MAE', 'R2', 'Mean_MAE']: + logging.info(f"{col}: Mean={results_df[col].mean()}\tstd={results_df[col].std()}") + + +if __name__ == '__main__': + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP Experiments") + parser.add_argument("--dqr_target", type=str, default="pc1", choices=["pc1", "mbfc", "mbfc_bias"], help="the credability target") + parser.add_argument("--dqr_text_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--dqr_gnn_emb_path", type=str, default=str(root + "/data/dqr") ,help="emb files path") + parser.add_argument("--dqr_path", type=str, default=str(root + "/data/dqr"),help="dqr dataset path") + parser.add_argument("--embed_type", type=str, default="text", + choices=["text", "domainName", "GNN_GAT", "TFIDF", "PASTEL"], help="domains embedding technique") + parser.add_argument("--emb_model", type=str, default="embeddingTE3L", + choices=["embeddingQwen3-8B", "embeddingQwen3-0.6B", "embeddinggemma-300m", "embeddingTE3L", + "IPTC_Topic_emb"],help="LLM embedding model") + parser.add_argument("--batch_size", type=int, default=5000,help="training batch size") + parser.add_argument("--test_valid_size", type=float, default=0.4,help="ratio of test and vaild sets") + parser.add_argument("--emb_dim", type=int, default=3072,help="embedding size") + parser.add_argument("--original_emb_dim", type=int, default=3072,help="The original embedding model dim size") + parser.add_argument("--max_iter", type=int, default=200,help="MLP regressor max iteration count") + parser.add_argument("--lr", type=float, default=5e-3,help="learning rate") + parser.add_argument("--epochs", type=int,default=100, help="# training epochs") + parser.add_argument("--plots_out_path", type=str, default=str(root + "/plots"),help="plots and results store path") + parser.add_argument("--logs_out_path", type=str, default=str(root + "/logs"),help="logging path") + parser.add_argument("--runs", type=int, default=1,help="# training runs") + parser.add_argument("--use_gnn_emb", action='store_false',help="append GNN embedding") + parser.add_argument("--gnn_encoder", default="text", choices=["RNI","text"],help="append GNN node embedding intialization") + parser.add_argument("--agg_month_emb", action='store_false', help="aggregate montly GNN embeddings") + parser.add_argument("--agg_text_emb", action='store_false', help="aggregate montly text embeddings") + parser.add_argument("--agg_function", type=str, default="avg",choices=["avg","cat"], help="aggregate function") + parser.add_argument("--use_topic_emb", action='store_true',help="use topic modeling features") + parser.add_argument("--filter_by_GNN_nodes", action='store_false',help="filter by domains has GNN embeddings") + parser.add_argument("--filter_by_PASTEL_domains", action='store_true',help="filter by domains has PASTEL embeddings") + parser.add_argument("--generate_weaksupervision_scores", type=bool, default=False, help="gnerate weak supervision datasets scores") + parser.add_argument("--month", type=str, default="dec", choices=["oct", "nov", "dec"],help="CrediBench month snapshot") + parser.add_argument("--library", type=str, default="pytorch", choices=["pytorch", "sklearn"],help="ML library to use") + args = parser.parse_args() + mlp_train(args) \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/regressor_transfer_inference.py b/tgrag/experiments/mlp_experiments/regressor_transfer_inference.py new file mode 100644 index 00000000..12a24a2d --- /dev/null +++ b/tgrag/experiments/mlp_experiments/regressor_transfer_inference.py @@ -0,0 +1,108 @@ +import argparse +import logging +from typing import Dict, cast +import pickle +import numpy as np +from tgrag.utils.args import parse_args +from tgrag.utils.logger import setup_logging +from tgrag.utils.path import get_root_dir +from tqdm import tqdm +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.neural_network import MLPRegressor +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error +from statistics import mean +import pyarrow.parquet as pq +import pyarrow.compute as pc +import multiprocessing +from multiprocessing import Pool, cpu_count +import threading +from utils import search_parquet_duckdb +def load_emb_dict(emb_pickle_path="domain_shard_index.pkl"): + embd_dict=None + with open(emb_pickle_path, 'rb') as f: + embd_dict = pickle.load(f) + return embd_dict + +def write_results(labels_annot_df,emb_file_name): + sources=["all_sources","legit-phish","misinfo-domains","nelez","phish-and-legit","phish-dataset","url-phish","urlhaus","wikipedia"] + acc_results=[] + for source in sources: + res=[] + res.append(source) + if source=="all_sources": + source_df=labels_annot_df + else: + source_df=labels_annot_df[~labels_annot_df[source].isna()] + res.append(len(source_df)) + for th in [0.1,0.3,0.5,0.7,0.9]: + k=f"pred_label_gte_{int(th*10)}" + filtered_df = source_df[source_df['binary_label'] == source_df[k]] + res.append(filtered_df[k].sum()/len(source_df)) + acc_results.append(res) + + res_df=pd.DataFrame(acc_results,columns=["source","count","gte_1","gte_3","gte_5","gte_7","gte_9"]) + res_df.to_csv(f"{emb_file_name.replace("_test_set_emb_dict.pkl","_ACC_results.csv")}",index=None) + +def main() -> None: + root = str(get_root_dir()) + parser = argparse.ArgumentParser(description="MLP transfer Inference") + parser.add_argument("--emb_path", type=str, default=str("/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/plots") ,help="text emb files path") + # parser.add_argument("--emb_file_name", type=str, default=str("weaksupervision_dec_text_TE3L_test_set_emb_dict.pkl") ,help="emb files name") + parser.add_argument("--emb_file_name", type=str, default=str("weaksupervision_dec_text_TE3L_GAT-text-avg_test_set_emb_dict.pkl") ,help="emb files name") + + parser.add_argument("--model_path", type=str, default=str("/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/plots") ,help="text emb files path") + # parser.add_argument("--model_file_name", type=str, default=str("dqr_dec_pc1_sklearn_text_embeddingTE3L__run1_credibench_MLP_Model.pkl") ,help="trained model name") + parser.add_argument("--model_file_name", type=str, default=str("dqr_dec_pc1_sklearn_text_embeddingTE3L_GAT-text-avg_run1_agg_credibench_MLP_Model.pkl") ,help="trained model name") + + parser.add_argument("--labels_path", type=str, default=str("/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/data/weaksupervision") ,help="text emb files path") + parser.add_argument("--labels_file_name", type=str, default=str("weaklabels.csv") ,help="labels_file_name") + args = parser.parse_args() + print("args=", args) + + ########## load lables ######## + labels_df=pd.read_csv(f"{args.labels_path}/{args.labels_file_name}") + labels_dict=dict(zip(labels_df["domain"].tolist(),labels_df["weak_label"].tolist())) + labels_annot_df=pd.read_csv(f"{args.labels_path}/labels_annot.csv") + mode="scores_ready" + if mode=="scores_ready": + # gat_parq_path="/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/data/weaksupervision/gat_inferred_scores_text_features_pc1_dec2024_from_binary_test.parquet" + gat_parq_path="/home/mila/a/abdallah/scratch/hsh_projects/CrediGraph/data/weaksupervision/gat_inferred_scores_rni_pc1_dec2024_from_binary_test.parquet" + reg_dict=search_parquet_duckdb(gat_parq_path, col=None,q_domains=None,max_memory="8GB",schema={'key':'domain','val':'scores'}) + reg_dict={".".join(k.split('.')[::-1]):v for k,v in reg_dict.items()} + emb_file_name=gat_parq_path.replace("_pc1_dec2024_from_binary_test.parquet","TL_test_set_emb_dict.pkl") + labels_annot_df=labels_annot_df[labels_annot_df["domain"].isin(reg_dict.keys())] + labels_annot_df["binary_label"]=labels_annot_df["domain"].apply(lambda x: labels_dict[x]) + else: + ############## Load model ############### + model_file_name = f"{args.model_path}/{args.model_file_name}" + mlp_reg=None + with open(model_file_name, 'rb') as f: + mlp_reg = pickle.load(f) + ############## Load emb ############### + emb_file_name = f"{args.emb_path}/{args.emb_file_name}" + emb_dict={} + with open(emb_file_name, 'rb') as f: + emb_dict = pickle.load(f) + labels_annot_df=labels_annot_df[labels_annot_df["domain"].isin(emb_dict.keys())] + labels_annot_df["binary_label"]=labels_annot_df["domain"].apply(lambda x: labels_dict[x]) + ############## Load emb index ############### + reg_dict={} + for k,v in tqdm(emb_dict.items()): + reg_dict[k]=mlp_reg.predict([v])[0] + + labels_annot_df["pred_pc1_score"]=labels_annot_df["domain"].apply(lambda x: reg_dict[x]) + for th in [0.1,0.3,0.5,0.7,0.9]: + labels_annot_df[f"pred_label_gte_{int(th*10)}"]=labels_annot_df["pred_pc1_score"].apply(lambda x: 1 if x>=th else 0) + write_results(labels_annot_df,emb_file_name) + labels_annot_df.to_csv(f"{emb_file_name.replace("_test_set_emb_dict.pkl","_pred_pc1_scores.csv")}",index=None) + labels_annot_df.to_parquet(f"{emb_file_name.replace("_test_set_emb_dict.pkl","_pred_pc1_scores.parquet")}", + engine='pyarrow',row_group_size=1000,use_dictionary=True,compression="snappy") + ##################### ACC Stats ################### + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tgrag/experiments/mlp_experiments/samplers.py b/tgrag/experiments/mlp_experiments/samplers.py index da8a6af3..a2748a03 100644 --- a/tgrag/experiments/mlp_experiments/samplers.py +++ b/tgrag/experiments/mlp_experiments/samplers.py @@ -11,18 +11,38 @@ def __init__(self, X, y): self.X = torch.as_tensor(X, dtype=torch.float32) self.y = torch.as_tensor(y, dtype=torch.float32) - def __len__(self): + def __len__(self): return self.X.shape[0] def __getitem__(self, idx): return self.X[idx], self.y[idx] +class MultiTaskDataset(Dataset): + def __init__(self, X_reg,X_cls, y_reg, y_cls): + """ + Parameters + ---------- + X_reg : torch.Tensor (N, D) + X_cls : torch.Tensor (N, D) + y_reg : torch.Tensor (N, 1) + y_cls : torch.Tensor (N,C) + """ + self.X_reg = torch.as_tensor(X_reg, dtype=torch.float32) + self.X_cls = torch.as_tensor(X_cls, dtype=torch.float32) + self.y_reg = torch.as_tensor(y_reg, dtype=torch.float32) + self.y_cls =torch.as_tensor(y_cls, dtype=torch.float32) + + def __len__(self): + return self.X_cls.shape[0] + + def __getitem__(self, idx): + return self.X_reg[idx[0]],self.X_cls[idx[1]], self.y_reg[idx[0]], self.y_cls[idx[1]] + class BalancedBatchSampler(Sampler): def __init__(self, labels, batch_size): self.labels = np.array(labels) self.batch_size = batch_size self.half = batch_size // 2 - self.idx_0 = np.where(self.labels == 0)[0] self.idx_1 = np.where(self.labels == 1)[0] @@ -46,4 +66,41 @@ def __iter__(self): ptr0 += self.half def __len__(self): - return len(self.idx_0) // self.half + return len(self.idx_0) // self.half + +class BalancedRegClsBatchSampler(Sampler): + def __init__(self, reg_labels,bcls_labels, batch_size): + self.bcls_labels = np.array(bcls_labels) + self.reg_labels = np.array(reg_labels) + self.batch_size = batch_size + self.half = batch_size // 2 + self.cls_idx_0 = np.where(self.bcls_labels == 0)[0] + self.cls_idx_1 = np.where(self.bcls_labels == 1)[0] + self.reg_idx = np.where(self.reg_labels >=0 )[0] + assert len(self.cls_idx_0) > 0 and len(self.cls_idx_1) > 0 and len(self.reg_idx) > 0 + + def __iter__(self): + np.random.shuffle(self.cls_idx_0) + np.random.shuffle(self.cls_idx_0) + np.random.shuffle(self.reg_idx) + for ptr0 in range(0, len(self.cls_idx_0),self.half): + bs=self.half + reg_ptr=ptr0 + rg_bs=self.half + if self.half + ptr0 > len(self.cls_idx_0): + bs = len(self.cls_idx_0) - ptr0 + rg_bs=bs + if rg_bs + reg_ptr > len(self.reg_idx): + reg_ptr= len(self.reg_idx)-rg_bs + + for ptr1 in range(0, len(self.cls_idx_1),bs): + batch = np.concatenate([ + list(zip(self.reg_idx[reg_ptr:reg_ptr + rg_bs],self.cls_idx_0[ptr0:ptr0 + bs])), + list(zip(self.reg_idx[reg_ptr:reg_ptr + rg_bs],self.cls_idx_1[ptr1:ptr1 + bs]))]) + np.random.shuffle(batch) + yield batch.tolist() + ptr1 += self.half + ptr0 += self.half + + def __len__(self): + return len(self.cls_idx_0) // self.half diff --git a/tgrag/experiments/mlp_experiments/utils.py b/tgrag/experiments/mlp_experiments/utils.py index 19bbf634..c769042e 100644 --- a/tgrag/experiments/mlp_experiments/utils.py +++ b/tgrag/experiments/mlp_experiments/utils.py @@ -13,7 +13,15 @@ import pyarrow.parquet as pq import duckdb import pyarrow as pa +from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay +import seaborn as sns +import os +import glob +import re +import logging +def list_all_files(root_path,rgex="*.pkl",recursive=False): + return glob.glob(f'{root_path}/{rgex}',recursive=recursive) def normalize_embeddings(emb_dict): norm_arr=normalize(list(emb_dict.values())) @@ -65,17 +73,17 @@ def resize_emb(text_emb, target, X_train, X_valid, X_test, gnn_emb=None, topic_e X_train_feat = [text_emb[d][0:trim_to] for d in X_train["domain"].tolist()] X_valid_feat = [text_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] X_test_feat = [text_emb[d][0:trim_to] for d in X_test["domain"].tolist()] - print("emb-size=", len(X_test_feat[0])) + logging.info(f"emb-size={len(X_test_feat[0])}") if gnn_emb is not None: - X_train_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_train["domain"].tolist()] + X_train_feat_gnn = [gnn_emb[d][0:256] for d in X_train["domain"].tolist()] X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_train_feat_gnn, X_train_feat)] - X_valid_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_valid["domain"].tolist()] + X_valid_feat_gnn = [gnn_emb[d][0:256] for d in X_valid["domain"].tolist()] X_valid_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_valid_feat_gnn, X_valid_feat)] - X_test_feat_gnn = [gnn_emb[d][0:trim_to] for d in X_test["domain"].tolist()] + X_test_feat_gnn = [gnn_emb[d][0:256] for d in X_test["domain"].tolist()] X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_gnn, X_test_feat)] - print("emb-size=", len(X_test_feat[0])) + logging.info(f"emb-size={len(X_test_feat[0])}") if topic_emb is not None: X_train_feat_topic = [topic_emb[d] for d in X_train["domain"].tolist()] X_train_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in @@ -87,7 +95,7 @@ def resize_emb(text_emb, target, X_train, X_valid, X_test, gnn_emb=None, topic_e X_test_feat_topic = [topic_emb[d] for d in X_test["domain"].tolist()] X_test_feat = [list(sublist1) + (list(sublist2)) for sublist1, sublist2 in zip(X_test_feat_topic, X_test_feat)] - print("emb-size=", len(X_test_feat[0])) + logging.info(f"emb-size={len(X_test_feat[0])}") return X_train_feat, X_valid_feat, X_test_feat @@ -109,7 +117,7 @@ def plot_loss(train_loss, valid_loss, test_loss, mean_loss, out_file_path="loss_ plt.show() -def plot_histogram(true, pred, out_file_path="_testset_true_vs_pred_frequancy.pdf",target="pc1"): +def plot_histogram(true, pred, out_file_path="_testset_true_vs_pred_frequency.pdf",target="pc1"): plt.figure(figsize=(5, 4)) plt.hist(pred, bins=50, range=(0, 1), edgecolor='black', color='lightblue', label="Pred") plt.hist(true, bins=50, range=(0, 1), edgecolor='black', color='orange', alpha=0.6, label="True") @@ -119,12 +127,12 @@ def plot_histogram(true, pred, out_file_path="_testset_true_vs_pred_frequancy.pd plt.xticks(np.arange(0, 1.1, 0.2), rotation=0, ha='right') plt.yticks(np.arange(0, y_max + 50, 100), rotation=0, ha='right') plt.xlabel(target.upper().replace("_", "-")) - plt.ylabel('Frequancy') + plt.ylabel('frequency') plt.legend() plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) plt.show() -def plot_classesCount(cm, out_file_path="_testset_true_vs_pred_frequancy.pdf",target="pc1"): +def plot_classesCount(cm, out_file_path="_testset_true_vs_pred_frequency.pdf",target="pc1"): plt.figure(figsize=(5, 4)) per_class_acc = np.diag(cm) / cm.sum(axis=1) per_class_acc = np.nan_to_num(per_class_acc) @@ -132,7 +140,7 @@ def plot_classesCount(cm, out_file_path="_testset_true_vs_pred_frequancy.pdf",ta plt.bar(classes, per_class_acc) plt.xlabel("Class Label") plt.ylabel("Accuracy") - plt.title("Per-Class Accuracy Derived from Confusion Matrix") + plt.title("Accuracy per Label") plt.ylim(0, 1) plt.xticks(classes, classes) for i, v in enumerate(per_class_acc): @@ -140,6 +148,18 @@ def plot_classesCount(cm, out_file_path="_testset_true_vs_pred_frequancy.pdf",ta plt.tight_layout() plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) plt.show() +def plot_confusion_matrix(cm, out_file_path="_testset_confusion_matrix.pdf"): + fig_size=max(3, len(cm)) + plt.figure(figsize=(fig_size, fig_size)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + xticklabels=[f'T:{idx}' for idx in range(len(cm))], + yticklabels=[f'P:{idx}' for idx in range(len(cm))]) + plt.ylabel('True') + plt.xlabel('Predicted') + plt.title('Confusion Matrix') + plt.tight_layout() + plt.savefig(out_file_path,bbox_inches='tight', pad_inches=0.1) + plt.show() @@ -172,13 +192,13 @@ def eval(pred, true): min_diff_dict["test_idx"] = min_idx mse = mean_squared_error(true, pred) - # print(f"mse={mse}") + # logging.info(f"mse={mse}") r2 = r2_score(true, pred) - # print(f"r2={r2}") + # logging.info(f"r2={r2}") mae = mean_absolute_error(true, pred) true_mean = mean(true) mean_mae = mean_absolute_error(true, [true_mean for elem in true]) - # print(f"MAE={mae}") + # logging.info(f"MAE={mae}") return mse, mae, r2, mean_mae, min_diff_dict, max_diff_dict diff --git a/tgrag/utils/logger.py b/tgrag/utils/logger.py index 50f8aa4a..8a488245 100644 --- a/tgrag/utils/logger.py +++ b/tgrag/utils/logger.py @@ -11,7 +11,6 @@ def setup_logging( stream_logging_level: int = logging.INFO, ) -> None: handlers: List[logging.Handler] = [] - stream_handler = logging.StreamHandler() stream_handler.setLevel(stream_logging_level) stream_handler.setFormatter( @@ -30,7 +29,7 @@ def setup_logging( handlers.append(file_handler) logging.basicConfig( - level=logging.DEBUG, + level=log_file_logging_level, format='[%(asctime)s] %(levelname)s [%(processName)s %(threadName)s %(name)s.%(funcName)s:%(lineno)d] %(message)s', handlers=handlers, )