citation_model
Model about paper citation (Kaggle competition)
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic links in README
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (0.6%) to scientific vocabulary
Last synced: 8 months ago
·
JSON representation
·
Repository
Model about paper citation (Kaggle competition)
Basic Info
- Host: GitHub
- Owner: marios4371
- Language: Jupyter Notebook
- Default Branch: main
- Size: 0 Bytes
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Created 10 months ago
· Last pushed 10 months ago
Metadata Files
Citation
Owner
- Login: marios4371
- Kind: user
- Repositories: 1
- Profile: https://github.com/marios4371
Citation (citations_model_new.ipynb)
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"cells": [
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')\n",
"\n",
"BASE_DIR = '/content/drive/MyDrive/data_nlp'\n",
"PRED_PATH = os.path.join(BASE_DIR, 'predictions.csv')"
],
"metadata": {
"id": "Y-TiamhcKD4y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0bbe6cb5-b51c-43af-b181-32cb8c81ccbb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Conncect with google drive and Importing the paths"
],
"metadata": {
"id": "tHJ88sgHKCCc"
}
},
{
"cell_type": "markdown",
"source": [
"install some packages"
],
"metadata": {
"id": "oncHg3LlKFFC"
}
},
{
"cell_type": "code",
"source": [
"!pip install --quiet spacy networkx scikit-learn joblib\n",
"!python -m spacy download en_core_web_sm\n",
"!pip install catboost"
],
"metadata": {
"id": "45VlNQpgKIWT",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "53a6a184-220e-433d-aca1-7995d302f9de"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting en-core-web-sm==3.8.0\n",
" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl (12.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m30.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the package via spacy.load('en_core_web_sm')\n",
"\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
"If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
"order to load all the package's dependencies. You can do this by selecting the\n",
"'Restart kernel' or 'Restart runtime' option.\n",
"Collecting catboost\n",
" Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n",
"Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from catboost) (0.20.3)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from catboost) (3.10.0)\n",
"Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from catboost) (2.0.2)\n",
"Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from catboost) (2.2.2)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from catboost) (1.15.3)\n",
"Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from catboost) (5.24.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from catboost) (1.17.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->catboost) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->catboost) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->catboost) (2025.2)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (1.3.2)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (4.58.0)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (1.4.8)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (24.2)\n",
"Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (11.2.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->catboost) (3.2.3)\n",
"Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->catboost) (9.1.2)\n",
"Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: catboost\n",
"Successfully installed catboost-1.2.8\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Importing the libraries"
],
"metadata": {
"id": "b9ZebQDKKKAz"
}
},
{
"cell_type": "code",
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import networkx as nx\n",
"import re\n",
"import joblib\n",
"import torch\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import roc_auc_score\n",
"from xgboost import XGBClassifier\n",
"from sentence_transformers import SentenceTransformer\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from catboost import CatBoostClassifier\n",
"from transformers import RobertaTokenizer, RobertaModel"
],
"metadata": {
"id": "tkA8j6shKLqr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Load the dataset"
],
"metadata": {
"id": "tYWYT-rVKM9b"
}
},
{
"cell_type": "code",
"source": [
"abstracts = pd.read_csv(f'{BASE_DIR}/abstracts.txt', sep=r'\\|\\-\\-\\|', engine='python', names=['id', 'text'], header=None)\n",
"authors = pd.read_csv(f'{BASE_DIR}/authors.txt', sep=r'\\|\\-\\-\\|', engine='python', names=['id', 'auth'], header=None)\n",
"edgelist = pd.read_csv(f'{BASE_DIR}/edgelist.txt', names=['src', 'tgt'], header=None)\n",
"\n",
"meta = abstracts.merge(authors, on='id').reset_index(drop=True)\n",
"all_ids = meta['id'].values\n",
"id2idx = {}\n",
"for i, pid in enumerate(all_ids):\n",
" id2idx[pid] = i"
],
"metadata": {
"id": "7CZZOkN_KOfy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Graph creation for identifying edges between papers"
],
"metadata": {
"id": "cC3k5T0FKQDj"
}
},
{
"cell_type": "code",
"source": [
"all_ids = meta['id'].values\n",
"id2idx = {pid:i for i,pid in enumerate(all_ids)}\n",
"G = nx.Graph()\n",
"G.add_edges_from(edgelist.values)\n",
"neighbors = {n:set(G.neighbors(n)) for n in G.nodes()}\n",
"degrees = {nid:G.degree(nid) for nid in all_ids}"
],
"metadata": {
"id": "2VU8wbqVKRmS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"precompute rage rank and clustering"
],
"metadata": {
"id": "9T3PVh_yRKj7"
}
},
{
"cell_type": "code",
"source": [
"pagerank = nx.pagerank(G)\n",
"clustering = nx.clustering(G)\n",
"pr_array = np.array([pagerank.get(pid, 0) for pid in all_ids], dtype=float)\n",
"clust_array = np.array([clustering.get(pid, 0) for pid in all_ids], dtype=float)"
],
"metadata": {
"id": "uf70NwIyROWr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Text preprocessing"
],
"metadata": {
"id": "u5Z7mcypKTIS"
}
},
{
"cell_type": "code",
"source": [
"raw_texts = meta['text'].fillna('').tolist()\n",
"proc_texts = [\" \".join(re.findall(r\"\\b[a-zA-Z]{3,}\\b\", t.lower())) for t in raw_texts]"
],
"metadata": {
"id": "OLYrubViKU4r"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"tfidf"
],
"metadata": {
"id": "H0uEoSLSQ2gM"
}
},
{
"cell_type": "code",
"source": [
"tfidf = TfidfVectorizer(ngram_range=(1, 2), max_features=5000)\n",
"X_tfidf = tfidf.fit_transform(proc_texts).toarray()\n",
"# Precompute top-k token sets for Jaccard\n",
"k = 50\n",
"topk_tokens = []\n",
"for vec in X_tfidf:\n",
" top_idx = np.argsort(vec)[-k:]\n",
" topk_tokens.append(set(top_idx))"
],
"metadata": {
"id": "h_6vGxoEQ4R0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Embeddings with SentenceTransformer()"
],
"metadata": {
"id": "Yb3g9_ObKWsT"
}
},
{
"cell_type": "code",
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"model_embed = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n",
"X_doc_vecs = model_embed.encode(proc_texts, show_progress_bar=True, batch_size=64, convert_to_numpy=True)\n"
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"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
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"Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
"WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
]
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{
"cell_type": "markdown",
"source": [
"roBERTa tokenization"
],
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}
},
{
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"source": [
"tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
"model_roberta = RobertaModel.from_pretrained('roberta-base').to('cuda')\n",
"model_roberta.eval()\n",
"\n",
"# tokenize and encoding in only one batch\n",
"\n",
"batch_size = 16 # ή ακόμα και 8\n",
"embeddings = []\n",
"for i in range(0, len(proc_texts), batch_size):\n",
" batch = proc_texts[i:i+batch_size]\n",
" enc = tokenizer(batch, padding='longest', truncation=True, max_length=128, return_tensors='pt').to('cuda')\n",
" with torch.no_grad():\n",
" out = model_roberta(**enc)\n",
" embeddings.append(out.last_hidden_state[:, 0, :].cpu().numpy())\n",
" torch.cuda.empty_cache()\n",
"\n",
"X_doc_vecs_roberta = np.vstack(embeddings)"
],
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{
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{
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"name": "stderr",
"text": [
"Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
"WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
]
},
{
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{
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"text": [
"Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Author sets for Jaccard Similarity"
],
"metadata": {
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}
},
{
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"source": [
"auth_sets = meta['auth'].fillna('').str.split(',').map(lambda L: {a.strip() for a in L}).tolist()\n"
],
"metadata": {
"id": "E_Bxk1F8Kbly"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Function for JaccardSimilarity"
],
"metadata": {
"id": "RPJHyMAJKdEr"
}
},
{
"cell_type": "code",
"source": [
"def jaccard(a, b):\n",
" return float(len(a & b)) / len(a | b) if (a | b) else 0.0"
],
"metadata": {
"id": "k5ILS3anKeiy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Negative/Positive pair creation"
],
"metadata": {
"id": "dyLjku0XKhEb"
}
},
{
"cell_type": "code",
"source": [
"pos = edgelist.values.tolist()\n",
"rng = np.random.default_rng(42)\n",
"neg = []\n",
"\n",
"while len(neg) < len(pos):\n",
" u, v = rng.choice(all_ids, 2, replace=False)\n",
" if not G.has_edge(u, v):\n",
" neg.append([u, v])\n",
"\n",
"pairs = np.array(pos + neg)\n",
"labels = np.array([1] * len(pos) + [0] * len(neg))"
],
"metadata": {
"id": "QYcb4NrlKhaC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Feature Extraction"
],
"metadata": {
"id": "tlh_FWABKiaL"
}
},
{
"cell_type": "code",
"source": [
"features = []\n",
"for u, v in pairs:\n",
" ui, vi = id2idx[u], id2idx[v]\n",
" # semantic embeddings\n",
" vec_u, vec_v = X_doc_vecs[ui], X_doc_vecs[vi]\n",
" sim = np.dot(vec_u, vec_v)\n",
" dist = np.linalg.norm(vec_u - vec_v)\n",
"\n",
" #roberta embeddings\n",
" vec_u1, vec_v1 = X_doc_vecs_roberta[ui], X_doc_vecs_roberta[vi]\n",
" sim_roberta = np.dot(vec_u1, vec_v1)\n",
" cos_roberta = sim_roberta / (\n",
" (np.linalg.norm(vec_u1) * np.linalg.norm(vec_v1)) + 1e-8\n",
" )\n",
" dist_roberta = np.linalg.norm(vec_u1 - vec_v1)\n",
"\n",
" # TF-IDF features\n",
" tfidf_sim = np.dot(X_tfidf[ui], X_tfidf[vi]) / (\n",
" np.linalg.norm(X_tfidf[ui]) * np.linalg.norm(X_tfidf[vi]) + 1e-8)\n",
" jacc_tfidf = jaccard(topk_tokens[ui], topk_tokens[vi])\n",
"\n",
" # authors\n",
" auth_sim = jaccard(auth_sets[ui], auth_sets[vi])\n",
"\n",
" # graph features\n",
" deg_diff = abs(degrees[ui] - degrees[vi])\n",
" cn = len(neighbors[u] & neighbors[v])\n",
" aa_score = sum(1 / np.log(degrees[id2idx[w]]) for w in (neighbors[u] & neighbors[v]) if degrees[id2idx[w]] > 1)\n",
" pr_diff = abs(pr_array[ui] - pr_array[vi])\n",
" clust_diff = abs(clust_array[ui] - clust_array[vi])\n",
"\n",
" # token lengths\n",
" tok_diff = abs(len(proc_texts[ui].split()) - len(proc_texts[vi].split()))\n",
" features.append([sim, dist, sim_roberta, cos_roberta, dist_roberta, tfidf_sim, jacc_tfidf, auth_sim,\n",
" deg_diff, cn, aa_score, pr_diff, clust_diff, tok_diff])\n",
"X = np.array(features)\n"
],
"metadata": {
"id": "7_97nXztKjwi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"scaling the features"
],
"metadata": {
"id": "9Vbwn4PERqch"
}
},
{
"cell_type": "code",
"source": [
"scaler = StandardScaler()\n",
"X = scaler.fit_transform(X)"
],
"metadata": {
"id": "eSLEspAzRr8o"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"train test split"
],
"metadata": {
"id": "QCxPh0i2Ru5R"
}
},
{
"cell_type": "code",
"source": [
"X_tr, X_val, y_tr, y_val = train_test_split(X, labels, test_size=0.1, stratify=labels, random_state=42)\n"
],
"metadata": {
"id": "L55tfrGvRwEY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"XGBoostmodel training"
],
"metadata": {
"id": "AyYrUt94K9eC"
}
},
{
"cell_type": "code",
"source": [
"xg_model = XGBClassifier(\n",
" objective='binary:logistic', n_jobs=-1, random_state=42,\n",
" max_depth=7, learning_rate=0.05, n_estimators=400,\n",
" subsample=0.8, colsample_bytree=0.8, scale_pos_weight=1\n",
")\n",
"xg_model.fit(X_tr, y_tr)"
],
"metadata": {
"id": "pfjgTFfdK_rK",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 253
},
"outputId": "cceaac85-795f-43a6-f8e6-3e4126ef9f4d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=7, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=400, n_jobs=-1,\n",
" num_parallel_tree=None, random_state=42, ...)"
],
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: #000;\n",
" --sklearn-color-text-muted: #666;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: flex;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
" align-items: start;\n",
" justify-content: space-between;\n",
" gap: 0.5em;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label .caption {\n",
" font-size: 0.6rem;\n",
" font-weight: lighter;\n",
" color: var(--sklearn-color-text-muted);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 0.5em;\n",
" text-align: center;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=7, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=400, n_jobs=-1,\n",
" num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=7, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=400, n_jobs=-1,\n",
" num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"source": [
"catboost training"
],
"metadata": {
"id": "QqBcY2q4egMy"
}
},
{
"cell_type": "code",
"source": [
"# catboost_params = {\n",
"# 'iterations': 300,\n",
"# 'learning_rate': 0.01,\n",
"# 'eval_metric': 'Accuracy',\n",
"# 'task_type': 'GPU',\n",
"# 'early_stopping_rounds': 20,\n",
"# 'verbose': 50\n",
"# }\n",
"\n",
"cat_model = CatBoostClassifier(\n",
" iterations=1000,\n",
" learning_rate=0.05,\n",
" depth=8,\n",
" loss_function='Logloss',\n",
" eval_metric='Accuracy',\n",
" random_seed=42,\n",
" od_type='Iter',\n",
" od_wait=50,\n",
" task_type='GPU',\n",
" verbose=100\n",
")\n",
"cat_model.fit(X_tr, y_tr)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nwOeXXPzejVQ",
"outputId": "220d3a64-b811-43d7-8fcc-82d67541c1b1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0:\tlearn: 0.9515207\ttotal: 312ms\tremaining: 5m 11s\n",
"100:\tlearn: 0.9594072\ttotal: 3.67s\tremaining: 32.7s\n",
"200:\tlearn: 0.9599292\ttotal: 8.6s\tremaining: 34.2s\n",
"300:\tlearn: 0.9601393\ttotal: 11.9s\tremaining: 27.6s\n",
"400:\tlearn: 0.9603072\ttotal: 15.1s\tremaining: 22.6s\n",
"500:\tlearn: 0.9604598\ttotal: 20.1s\tremaining: 20s\n",
"600:\tlearn: 0.9605962\ttotal: 23.3s\tremaining: 15.5s\n",
"700:\tlearn: 0.9607035\ttotal: 26.5s\tremaining: 11.3s\n",
"800:\tlearn: 0.9608236\ttotal: 29.7s\tremaining: 7.38s\n",
"900:\tlearn: 0.9609518\ttotal: 34.6s\tremaining: 3.8s\n",
"999:\tlearn: 0.9610418\ttotal: 37.8s\tremaining: 0us\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<catboost.core.CatBoostClassifier at 0x7beb95de0b90>"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"source": [
"svm"
],
"metadata": {
"id": "L7FUcaXSF0-X"
}
},
{
"cell_type": "code",
"source": [
"from sklearn.svm import SVC\n",
"\n",
"svm_model = SVC(kernel='linear', probability=True, random_state=42)\n",
"svm_model.fit(X_tr, y_tr)\n",
"\n"
],
"metadata": {
"id": "Rr_4aR1lF2tI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"evaluation"
],
"metadata": {
"id": "EsJXghW1Kq3S"
}
},
{
"cell_type": "code",
"source": [
"\n",
"val_preds_xg = xg_model.predict_proba(X_val)[:, 1]\n",
"val_preds_cat = cat_model.predict_proba(X_val)[:, 1]\n",
"final_probs_AUC = (xg_preds + cat_preds) / 2\n",
"print(\"Validation AUC:\", roc_auc_score(y_val, final_probs_AUC))\n",
"# val_preds_svm = svm_model.predict_proba(X_val)[:, 1]\n",
"# print(\"validation AUC:\", roc_auc_score(y_val, val_preds_svm))\n"
],
"metadata": {
"id": "q-i6_55YKt0a"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"test prediction"
],
"metadata": {
"id": "8VUicNiMKvpj"
}
},
{
"cell_type": "code",
"source": [
"pairs_test = pd.read_csv(os.path.join(BASE_DIR, 'test.txt'), names=['src', 'tgt']).values\n",
"# extract test features using same logic\n",
"features_test = []\n",
"for u, v in pairs_test:\n",
"\n",
" #semantic embeddings\n",
" ui, vi = id2idx[u], id2idx[v]\n",
" vec_u, vec_v = X_doc_vecs[ui], X_doc_vecs[vi]\n",
" sim = np.dot(vec_u, vec_v)\n",
" dist = np.linalg.norm(vec_u - vec_v)\n",
"\n",
" #roberta embeddings\n",
" vec_u1, vec_v1 = X_doc_vecs_roberta[ui], X_doc_vecs_roberta[vi]\n",
" sim_roberta = np.dot(vec_u1, vec_v1)\n",
" cos_roberta = sim_roberta / (\n",
" (np.linalg.norm(vec_u1) * np.linalg.norm(vec_v1)) + 1e-8\n",
" )\n",
" dist_roberta = np.linalg.norm(vec_u1 - vec_v1)\n",
"\n",
" #tf-idf features\n",
" tfidf_sim = np.dot(X_tfidf[ui], X_tfidf[vi]) / (\n",
" np.linalg.norm(X_tfidf[ui]) * np.linalg.norm(X_tfidf[vi]) + 1e-8)\n",
" jacc_tfidf = jaccard(topk_tokens[ui], topk_tokens[vi])\n",
"\n",
" #authors\n",
" auth_sim = jaccard(auth_sets[ui], auth_sets[vi])\n",
"\n",
" # graph features\n",
" deg_diff = abs(degrees[ui] - degrees[vi])\n",
" cn = len(neighbors[u] & neighbors[v])\n",
" aa_score = sum(1 / np.log(degrees[id2idx[w]]) for w in (neighbors[u] & neighbors[v]) if degrees[id2idx[w]] > 1)\n",
" pr_diff = abs(pr_array[ui] - pr_array[vi])\n",
" clust_diff = abs(clust_array[ui] - clust_array[vi])\n",
"\n",
" # token lengths\n",
" tok_diff = abs(len(proc_texts[ui].split()) - len(proc_texts[vi].split()))\n",
" features_test.append([sim, dist, sim_roberta, cos_roberta, dist_roberta, tfidf_sim, jacc_tfidf, auth_sim,\n",
" deg_diff, cn, aa_score, pr_diff, clust_diff, tok_diff])\n",
"\n",
"# normalization\n",
"X_test = scaler.transform(np.array(features_test))\n",
"\n",
"xg_preds = xg_model.predict_proba(X_test)[:, 1]\n",
"cat_preds = cat_model.predict_proba(X_test)[:, 1]\n",
"\n",
"final_probs = (xg_preds + cat_preds) / 2\n",
"# final_probs = svm_model.predict_proba(X_test)[:, 1]"
],
"metadata": {
"id": "SlLpWu13KxlC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Model prediction and save .csv file"
],
"metadata": {
"id": "xKV2npRRK4Ja"
}
},
{
"cell_type": "code",
"source": [
"pd.DataFrame({'ID': range(len(final_probs)), 'label': final_probs}).to_csv(PRED_PATH, index=False, float_format='%.6f')\n"
],
"metadata": {
"id": "0p1-qkmGK4h7"
},
"execution_count": null,
"outputs": []
}
]
}
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