Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
○Academic links in README
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Unable to calculate vocabulary similarity
Last synced: 10 months ago
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·
Repository
Basic Info
- Host: GitHub
- Owner: Alexander-Belyi
- Language: Jupyter Notebook
- Default Branch: main
- Size: 43.2 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 3 years ago
· Last pushed over 2 years ago
Metadata Files
Citation
Citation (citation_prediction.ipynb)
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import json\n",
"import csv\n",
"import dill # dill is a more powerful version of pickle\n",
"import pandas as pd\n",
"import networkx as nx\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec\n",
"plt.style.use('ggplot')\n",
"#plt.rcParams[\"figure.figsize\"] = (20,3)\n",
"from tqdm import tqdm\n",
"\n",
"from scipy.spatial.distance import cdist, euclidean\n",
"from sklearn.metrics import roc_auc_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import torch.nn.functional as F\n",
"\n",
"import torch_geometric\n",
"import torch_geometric.utils\n",
"from torch_geometric.utils import from_networkx, negative_sampling, train_test_split_edges\n",
"from torch_geometric.transforms import RandomLinkSplit\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"data_path = './scidocs/data/'\n",
"paper_cite_file = data_path + 'paper_metadata_view_cite_read.json'\n",
"paper_cls_file = data_path + 'paper_metadata_mag_mesh.json'\n",
"paper_rec_file = data_path + 'paper_metadata_recomm.json'\n",
"user_activity_and_citations_embeddings_path = data_path + 'specter-embeddings/user-citation.jsonl'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Look at the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"papers_data = {}\n",
"with open(paper_cite_file, 'r') as f:\n",
" papers_data = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['abstract', 'authors', 'cited_by', 'paper_id', 'references', 'title', 'year']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(list(papers_data.values())[0].keys())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# papers_data = {}\n",
"# with open(paper_rec_file, 'r') as f:\n",
"# papers_data = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['abstract', 'authors', 'cited_by', 'paper_id', 'references', 'title', 'year']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(list(papers_data.values())[0].keys())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"papers_df = pd.DataFrame(papers_data).T"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#extra column\n",
"all(papers_df.index == papers_df.paper_id)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>abstract</th>\n",
" <th>authors</th>\n",
" <th>cited_by</th>\n",
" <th>paper_id</th>\n",
" <th>references</th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0003aa77bdefc1c75f9d2ba732635c132fc0c863</th>\n",
" <td>PROBLEM STATEMENT\\nPelvic girdle pain (PGP) is...</td>\n",
" <td>[40572137, 3675075, 48815127]</td>\n",
" <td>[e7e7a7fc07f516fd39b4b0cb7ff3a2acfe837c1a, e87...</td>\n",
" <td>0003aa77bdefc1c75f9d2ba732635c132fc0c863</td>\n",
" <td>[420604a2d0161cd5b5d2df75dd6252f224c8b055, e78...</td>\n",
" <td>Pelvic Girdle Pain during or after Pregnancy: ...</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0007181efc556fd1fcda2642e9bd85dd0f0c32d6</th>\n",
" <td>Routers must perform packet classification at ...</td>\n",
" <td>[23633340, 1688025, 1746289]</td>\n",
" <td>[76d64770fc8f032d047b034650c666e2c731c87e, bfb...</td>\n",
" <td>0007181efc556fd1fcda2642e9bd85dd0f0c32d6</td>\n",
" <td>[0e541308cc7c5ce8574bab03c090b6a0c5c6355b, 1a1...</td>\n",
" <td>Packet Classification Using Tuple Space Search</td>\n",
" <td>1999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000c009765a276d166fc67595e107a9bc44f230d</th>\n",
" <td>The data of interest are assumed to be represe...</td>\n",
" <td>[1869497, 3143096, 1746676]</td>\n",
" <td>[f354b0103c4bc8cb14bed77a27f5f4ffe580efdb, 7bf...</td>\n",
" <td>000c009765a276d166fc67595e107a9bc44f230d</td>\n",
" <td>[d68725804eadecf83d707d89e12c5132bf376187, 57b...</td>\n",
" <td>Bayesian Compressive Sensing</td>\n",
" <td>2008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000f90380d768a85e2316225854fc377c079b5c4</th>\n",
" <td>Semantic image segmentation is an essential co...</td>\n",
" <td>[3408089, 36665147, 11983029, 1789756]</td>\n",
" <td>[c2c0fda9b4e2a12fd4069ab545e90ec4a197e66d, 1fa...</td>\n",
" <td>000f90380d768a85e2316225854fc377c079b5c4</td>\n",
" <td>[981fef7155742608b8b6673f4a9566158b76cd67, 942...</td>\n",
" <td>Full-Resolution Residual Networks for Semantic...</td>\n",
" <td>2017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00111610254bfb8ec16428501c2ca68dcf817474</th>\n",
" <td>We introduce the social study of bullying to t...</td>\n",
" <td>[1729642, 2610963, 1832364, 3009549]</td>\n",
" <td>[6618b4dbea9cc0a229e603c0326eac957c420ac4, 49b...</td>\n",
" <td>00111610254bfb8ec16428501c2ca68dcf817474</td>\n",
" <td>[639c1ec9edcbca7aa80ab56a52487def431aed5e, 899...</td>\n",
" <td>Learning from Bullying Traces in Social Media</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" abstract \\\n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 PROBLEM STATEMENT\\nPelvic girdle pain (PGP) is... \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 Routers must perform packet classification at ... \n",
"000c009765a276d166fc67595e107a9bc44f230d The data of interest are assumed to be represe... \n",
"000f90380d768a85e2316225854fc377c079b5c4 Semantic image segmentation is an essential co... \n",
"00111610254bfb8ec16428501c2ca68dcf817474 We introduce the social study of bullying to t... \n",
"\n",
" authors \\\n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 [40572137, 3675075, 48815127] \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 [23633340, 1688025, 1746289] \n",
"000c009765a276d166fc67595e107a9bc44f230d [1869497, 3143096, 1746676] \n",
"000f90380d768a85e2316225854fc377c079b5c4 [3408089, 36665147, 11983029, 1789756] \n",
"00111610254bfb8ec16428501c2ca68dcf817474 [1729642, 2610963, 1832364, 3009549] \n",
"\n",
" cited_by \\\n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 [e7e7a7fc07f516fd39b4b0cb7ff3a2acfe837c1a, e87... \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 [76d64770fc8f032d047b034650c666e2c731c87e, bfb... \n",
"000c009765a276d166fc67595e107a9bc44f230d [f354b0103c4bc8cb14bed77a27f5f4ffe580efdb, 7bf... \n",
"000f90380d768a85e2316225854fc377c079b5c4 [c2c0fda9b4e2a12fd4069ab545e90ec4a197e66d, 1fa... \n",
"00111610254bfb8ec16428501c2ca68dcf817474 [6618b4dbea9cc0a229e603c0326eac957c420ac4, 49b... \n",
"\n",
" paper_id \\\n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 0003aa77bdefc1c75f9d2ba732635c132fc0c863 \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 0007181efc556fd1fcda2642e9bd85dd0f0c32d6 \n",
"000c009765a276d166fc67595e107a9bc44f230d 000c009765a276d166fc67595e107a9bc44f230d \n",
"000f90380d768a85e2316225854fc377c079b5c4 000f90380d768a85e2316225854fc377c079b5c4 \n",
"00111610254bfb8ec16428501c2ca68dcf817474 00111610254bfb8ec16428501c2ca68dcf817474 \n",
"\n",
" references \\\n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 [420604a2d0161cd5b5d2df75dd6252f224c8b055, e78... \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 [0e541308cc7c5ce8574bab03c090b6a0c5c6355b, 1a1... \n",
"000c009765a276d166fc67595e107a9bc44f230d [d68725804eadecf83d707d89e12c5132bf376187, 57b... \n",
"000f90380d768a85e2316225854fc377c079b5c4 [981fef7155742608b8b6673f4a9566158b76cd67, 942... \n",
"00111610254bfb8ec16428501c2ca68dcf817474 [639c1ec9edcbca7aa80ab56a52487def431aed5e, 899... \n",
"\n",
" title \\\n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 Pelvic Girdle Pain during or after Pregnancy: ... \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 Packet Classification Using Tuple Space Search \n",
"000c009765a276d166fc67595e107a9bc44f230d Bayesian Compressive Sensing \n",
"000f90380d768a85e2316225854fc377c079b5c4 Full-Resolution Residual Networks for Semantic... \n",
"00111610254bfb8ec16428501c2ca68dcf817474 Learning from Bullying Traces in Social Media \n",
"\n",
" year \n",
"0003aa77bdefc1c75f9d2ba732635c132fc0c863 2013 \n",
"0007181efc556fd1fcda2642e9bd85dd0f0c32d6 1999 \n",
"000c009765a276d166fc67595e107a9bc44f230d 2008 \n",
"000f90380d768a85e2316225854fc377c079b5c4 2017 \n",
"00111610254bfb8ec16428501c2ca68dcf817474 2012 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"papers_df.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`get_graph` function"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_graph(file_name):\n",
" # Retreive the papers data\n",
" papers_data = {}\n",
" with open(file_name, 'r') as f:\n",
" papers_data = json.loads(f.read())\n",
" \n",
" # Create a directed graph from the papers data\n",
" G = nx.DiGraph()\n",
" G.add_nodes_from(papers_data.keys())\n",
"\n",
" # Add edges to the graph based on the citations\n",
" for paper_id, paper_attrs in papers_data.items():\n",
" for citing_id in paper_attrs['cited_by']:\n",
" if citing_id in G:\n",
" G.add_edge(citing_id, paper_id)\n",
" for cited_id in paper_attrs['references']:\n",
" if cited_id in G:\n",
" G.add_edge(paper_id, cited_id)\n",
"\n",
" return G"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build graph from `paper_cite_file`"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"G = get_graph(paper_cite_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional information about the graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of nodes and edges"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(142009, 834049)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(G), G.number_of_edges()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for node info"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def get_nodes_info(G):\n",
" \"\"\"\n",
" Print the nodes info from the graph\n",
" ## Parameters\n",
" G : networkx.Graph\n",
" The graph to get the nodes info from\n",
" \n",
" \"\"\"\n",
" # info about node degrees\n",
" degrees = [d for n, d in G.degree()]\n",
" print('\\nNodes degrees info\\nmean:', np.mean(degrees),\n",
" '\\nmedian:', np.median(degrees),\n",
" '\\nmax:', np.max(degrees),\n",
" '\\nmin:', np.min(degrees)\n",
" )\n",
"\n",
" # info about nodes in-degrees\n",
" degrees = [d for n, d in G.in_degree()]\n",
" print('\\nNodes in-degrees info\\nmean:', np.mean(degrees),\n",
" '\\nmedian:', np.median(degrees),\n",
" '\\nmax:', np.max(degrees),\n",
" '\\nmin:', np.min(degrees)\n",
" )\n",
"\n",
" # info about node in-degrees\n",
" degrees = [d for n, d in G.out_degree()]\n",
" print('\\nNodes out-degrees info\\nmean:', np.mean(degrees),\n",
" '\\nmedian:', np.median(degrees),\n",
" '\\nmax:', np.max(degrees),\n",
" '\\nmin:', np.min(degrees)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Nodes degrees info\n",
"mean: 11.746424522389425 \n",
"median: 5.0 \n",
"max: 4193 \n",
"min: 0\n",
"\n",
"Nodes in-degrees info\n",
"mean: 5.873212261194713 \n",
"median: 1.0 \n",
"max: 4174 \n",
"min: 0\n",
"\n",
"Nodes out-degrees info\n",
"mean: 5.873212261194713 \n",
"median: 3.0 \n",
"max: 484 \n",
"min: 0\n"
]
}
],
"source": [
"get_nodes_info(G)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('2315fc6c2c0c4abd2443e26a26e7bb86df8e24cc', 4174),\n",
" ('272216c1f097706721096669d85b2843c23fa77d', 2986),\n",
" ('061356704ec86334dbbc073985375fe13cd39088', 2848),\n",
" ('2c03df8b48bf3fa39054345bafabfeff15bfd11d', 2701),\n",
" ('01fcae344d2edb715bcc63a40b6052c0331741bd', 2676),\n",
" ('0b3cfbf79d50dae4a16584533227bb728e3522aa', 2500),\n",
" ('1aec04aa64f165bb075cc4ce6ad79d36c89d62b6', 2487),\n",
" ('c829b63a3ae72a47e1953e1295826c7b2f93bf50', 1778),\n",
" ('cbcd9f32b526397f88d18163875d04255e72137f', 1568),\n",
" ('14318685b5959b51d0f1e3db34643eb2855dc6d9', 1561),\n",
" ('0b99d677883883584d9a328f6f2d54738363997a', 1557),\n",
" ('722fcc35def20cfcca3ada76c8dd7a585d6de386', 1481),\n",
" ('1827de6fa9c9c1b3d647a9d707042e89cf94abf0', 1399),\n",
" ('54dd77bd7b904a6a69609c9f3af11b42f654ab5d', 1391),\n",
" ('1e56ed3d2c855f848ffd91baa90f661772a279e1', 1371),\n",
" ('6c8b30f63f265c32e26d999aa1fef5286b8308ad', 1349),\n",
" ('0a10d64beb0931efdc24a28edaa91d539194b2e2', 1346),\n",
" ('4afa6c2eb552ceef0e396fbfe449932492873034', 1341),\n",
" ('0825788b9b5a18e3dfea5b0af123b5e939a4f564', 1328),\n",
" ('39dba6f22d72853561a4ed684be265e179a39e4f', 1319),\n",
" ('154728875d4668065ca6ba9fa2f5d2a1bcfc4a6e', 1313),\n",
" ('10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5', 1294),\n",
" ('981fef7155742608b8b6673f4a9566158b76cd67', 1264),\n",
" ('9201bf6f8222c2335913002e13fbac640fc0f4ec', 1206),\n",
" ('6de2b1058c5b717878cce4e7e50d3a372cc4aaa6', 1202)]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sorted list of nodes by in-degree\n",
"sorted_nodes = sorted(G.in_degree(), key=lambda x: x[1], reverse=True)\n",
"sorted_nodes[:25]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connected components"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function to retreive connected components and their sizes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def get_conn_comp(G):\n",
" \"\"\"\n",
" Get connected components of the graph and their sizes\n",
"\n",
" ## Parameters\n",
" G : networkx.Graph\n",
" \n",
" ## Returns\n",
" conn_comps : dictionary\n",
" A dictionary where key is the size of the component and value is the nodes in component\n",
" conn_comp_sizes : list\n",
" A list of sizes of the connected components\n",
" \"\"\"\n",
" # Create connected components dictionary where key is the size of the component and value is the nodes in component\n",
" conn_comps = {len(c):c for c in nx.weakly_connected_components(G)}\n",
" conn_comp_sizes = conn_comps.keys()\n",
" \n",
" return conn_comps, conn_comp_sizes\n",
"\n",
"_, conn_comp_sizes = get_conn_comp(G)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[120188, 34, 32, 23, 18, 15, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(conn_comp_sizes, reverse=True)[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find the suitable k-core decomposition subgraph "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decomposition function"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def get_core_decomposition_subgraph(G, k):\n",
" \"\"\"\n",
" Perform core decomposition to extract the k-core of the graph.\n",
" \n",
" ## Parameters:\n",
" G : networkx.Graph\n",
" k : int\n",
" The minimum degree of the k-core\n",
" \n",
" ## Returns:\n",
" k_core_subgraph : networkx.Graph\n",
" The k-core of the graph as a NetworkX graph\n",
" \"\"\"\n",
" # Extract the k-core of the graph\n",
" k_core_subgraph = nx.k_core(G, k=k)\n",
" \n",
" return k_core_subgraph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Look at the number of nodes for each decomposition"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k: 3\n",
"nodes: 92784\n",
"k: 6\n",
"nodes: 59552\n",
"k: 9\n",
"nodes: 37005\n",
"k: 12\n",
"nodes: 22599\n",
"k: 15\n",
"nodes: 14974\n",
"k: 16\n",
"nodes: 13168\n",
"k: 17\n",
"nodes: 11495\n",
"k: 18\n",
"nodes: 10093\n",
"k: 19\n",
"nodes: 8734\n",
"k: 20\n",
"nodes: 7331\n",
"k: 21\n",
"nodes: 5936\n",
"k: 22\n",
"nodes: 4772\n",
"k: 23\n",
"nodes: 3875\n",
"k: 24\n",
"nodes: 3128\n"
]
}
],
"source": [
"for k in range(3, 15+1, 3):\n",
" print('k:', k)\n",
" k_core_subgraph = get_core_decomposition_subgraph(G, k=k)\n",
" print('nodes:', len(k_core_subgraph))\n",
"\n",
"for k in range(16, 24+1, 1):\n",
" print('k:', k)\n",
" k_core_subgraph = get_core_decomposition_subgraph(G, k=k)\n",
" print('nodes:', len(k_core_subgraph))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Based on the results k=20 is the optimal subgraph"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Nodes degrees info\n",
"mean: 44.129859500750236 \n",
"median: 30.0 \n",
"max: 2182 \n",
"min: 20\n",
"\n",
"Nodes in-degrees info\n",
"mean: 22.064929750375118 \n",
"median: 5.0 \n",
"max: 2164 \n",
"min: 0\n",
"\n",
"Nodes out-degrees info\n",
"mean: 22.064929750375118 \n",
"median: 22.0 \n",
"max: 339 \n",
"min: 0\n"
]
}
],
"source": [
"G = get_core_decomposition_subgraph(G, k=20)\n",
"get_nodes_info(G)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('2315fc6c2c0c4abd2443e26a26e7bb86df8e24cc', 2164),\n",
" ('061356704ec86334dbbc073985375fe13cd39088', 1737),\n",
" ('2c03df8b48bf3fa39054345bafabfeff15bfd11d', 1681),\n",
" ('01fcae344d2edb715bcc63a40b6052c0331741bd', 1681),\n",
" ('272216c1f097706721096669d85b2843c23fa77d', 1392),\n",
" ('0b3cfbf79d50dae4a16584533227bb728e3522aa', 1267),\n",
" ('1aec04aa64f165bb075cc4ce6ad79d36c89d62b6', 1265),\n",
" ('14318685b5959b51d0f1e3db34643eb2855dc6d9', 1011),\n",
" ('0b99d677883883584d9a328f6f2d54738363997a', 1011),\n",
" ('54dd77bd7b904a6a69609c9f3af11b42f654ab5d', 884),\n",
" ('981fef7155742608b8b6673f4a9566158b76cd67', 826),\n",
" ('1827de6fa9c9c1b3d647a9d707042e89cf94abf0', 813),\n",
" ('009fba8df6bbca155d9e070a9bd8d0959bc693c2', 811),\n",
" ('722fcc35def20cfcca3ada76c8dd7a585d6de386', 806),\n",
" ('9201bf6f8222c2335913002e13fbac640fc0f4ec', 793),\n",
" ('39dba6f22d72853561a4ed684be265e179a39e4f', 756),\n",
" ('154728875d4668065ca6ba9fa2f5d2a1bcfc4a6e', 756),\n",
" ('cbcd9f32b526397f88d18163875d04255e72137f', 748),\n",
" ('6de2b1058c5b717878cce4e7e50d3a372cc4aaa6', 727),\n",
" ('d1e4a85ce2203fafa8247f5638bb09bcf919754a', 705),\n",
" ('424561d8585ff8ebce7d5d07de8dbf7aae5e7270', 705),\n",
" ('c829b63a3ae72a47e1953e1295826c7b2f93bf50', 701),\n",
" ('5e0f8c355a37a5a89351c02f174e7a5ddcb98683', 645),\n",
" ('6c8b30f63f265c32e26d999aa1fef5286b8308ad', 625),\n",
" ('0825788b9b5a18e3dfea5b0af123b5e939a4f564', 609)]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sorted list of nodes by in-degree\n",
"sorted_nodes = sorted(G.in_degree(), key=lambda x: x[1], reverse=True)\n",
"sorted_nodes[:25]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Look at the connected components"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[7331]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_, conn_comp_sizes = get_conn_comp(G)\n",
"sorted(conn_comp_sizes, reverse=True)[:20]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# G = get_graph(paper_cite_file)\n",
"# G = get_core_decomposition_subgraph(G, k=20)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7331, 161758)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(G), len(G.edges)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add embedding as a node attribute"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download embeddings"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"reading embeddings from file...: 142009it [00:37, 3789.23it/s]\n"
]
}
],
"source": [
"def load_embeddings_from_jsonl(embeddings_path, G):\n",
" embeddings = {}\n",
" with open(embeddings_path, 'r') as f:\n",
" for line in tqdm(f, desc='reading embeddings from file...'):\n",
" line_json = json.loads(line)\n",
" if line_json['paper_id'] in G:\n",
" embeddings[line_json['paper_id']] = np.array(line_json['embedding'], dtype=np.float32)\n",
" return embeddings\n",
"\n",
"def load_embeddings_from_json(embeddings_path, G):\n",
" embs = {}\n",
" with open(embeddings_path, 'r') as f:\n",
" all_embs = json.load(f)\n",
" for id, emb in tqdm(all_embs.items(), desc='reading embeddings from file...'):\n",
" if id in G.nodes:\n",
" embs[id] = np.array(emb, dtype=np.float32)\n",
" return embs\n",
"\n",
"embeddings = load_embeddings_from_jsonl(user_activity_and_citations_embeddings_path, G)\n",
"#embeddings = load_embeddings_from_json('papers_embeddings_sp1.json', G)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Amount of resourses with embeddings"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"7331"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(embeddings.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a subgraph based on resourses with embs"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"G_cite = G.subgraph(embeddings.keys())\n",
"nx.set_node_attributes(G_cite, {node_id: {\"x\":embedding} for node_id, embedding in embeddings.items()})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Information about the graph"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7331, 161758, 7331)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(G_cite), len(G_cite.edges), len(embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Nodes degrees info\n",
"mean: 44.129859500750236 \n",
"median: 30.0 \n",
"max: 2182 \n",
"min: 20\n",
"\n",
"Nodes in-degrees info\n",
"mean: 22.064929750375118 \n",
"median: 5.0 \n",
"max: 2164 \n",
"min: 0\n",
"\n",
"Nodes out-degrees info\n",
"mean: 22.064929750375118 \n",
"median: 22.0 \n",
"max: 339 \n",
"min: 0\n"
]
}
],
"source": [
"get_nodes_info(G_cite)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('2315fc6c2c0c4abd2443e26a26e7bb86df8e24cc', 2164),\n",
" ('061356704ec86334dbbc073985375fe13cd39088', 1737),\n",
" ('2c03df8b48bf3fa39054345bafabfeff15bfd11d', 1681),\n",
" ('01fcae344d2edb715bcc63a40b6052c0331741bd', 1681),\n",
" ('272216c1f097706721096669d85b2843c23fa77d', 1392),\n",
" ('0b3cfbf79d50dae4a16584533227bb728e3522aa', 1267),\n",
" ('1aec04aa64f165bb075cc4ce6ad79d36c89d62b6', 1265),\n",
" ('14318685b5959b51d0f1e3db34643eb2855dc6d9', 1011),\n",
" ('0b99d677883883584d9a328f6f2d54738363997a', 1011),\n",
" ('54dd77bd7b904a6a69609c9f3af11b42f654ab5d', 884),\n",
" ('981fef7155742608b8b6673f4a9566158b76cd67', 826),\n",
" ('1827de6fa9c9c1b3d647a9d707042e89cf94abf0', 813),\n",
" ('009fba8df6bbca155d9e070a9bd8d0959bc693c2', 811),\n",
" ('722fcc35def20cfcca3ada76c8dd7a585d6de386', 806),\n",
" ('9201bf6f8222c2335913002e13fbac640fc0f4ec', 793),\n",
" ('39dba6f22d72853561a4ed684be265e179a39e4f', 756),\n",
" ('154728875d4668065ca6ba9fa2f5d2a1bcfc4a6e', 756),\n",
" ('cbcd9f32b526397f88d18163875d04255e72137f', 748),\n",
" ('6de2b1058c5b717878cce4e7e50d3a372cc4aaa6', 727),\n",
" ('d1e4a85ce2203fafa8247f5638bb09bcf919754a', 705),\n",
" ('424561d8585ff8ebce7d5d07de8dbf7aae5e7270', 705),\n",
" ('c829b63a3ae72a47e1953e1295826c7b2f93bf50', 701),\n",
" ('5e0f8c355a37a5a89351c02f174e7a5ddcb98683', 645),\n",
" ('6c8b30f63f265c32e26d999aa1fef5286b8308ad', 625),\n",
" ('0825788b9b5a18e3dfea5b0af123b5e939a4f564', 609)]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sorted list of nodes by in-degree\n",
"sorted_nodes = sorted(G_cite.in_degree(), key=lambda x: x[1], reverse=True)\n",
"sorted_nodes[:25]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload the final graph"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"with open(data_path + 'cite_graph_k_20.pkl', 'wb') as f:\n",
" dill.dump(G_cite, f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Link prediction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create LinkPredictionModel class"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"class LinkPredictionModel(torch.nn.Module):\n",
"\n",
" def __init__(self, layer_type, sz_in, num_layers=2, sz_hid=128, sz_out=64):\n",
" super().__init__()\n",
"\n",
" # GNN layers with ReLU\n",
" encoder = []\n",
" encoder.append(layer_type(sz_in, sz_hid))\n",
" encoder.append(nn.ReLU())\n",
" for _ in range(num_layers-2):\n",
" encoder.append(layer_type(sz_hid, sz_hid))\n",
" encoder.append(nn.ReLU())\n",
" encoder.append(layer_type(sz_hid, sz_out))\n",
" self.encoder = nn.ModuleList(encoder)\n",
" #self.H = None # torch.tensor(sz_in, sz_out)\n",
" \n",
" # Encoding: usual GNN propagation\n",
" def encode(self, fts, adj):\n",
" for l in self.encoder:\n",
" if isinstance(l, nn.ReLU):\n",
" fts = l(fts)\n",
" else:\n",
" fts = l(fts, adj)\n",
" #self.H = fts\n",
" return fts\n",
" \n",
" # Decoding: dot(H[i], H[j]) for each edge in edge_index\n",
" # Larger dot => the model is more confident that this edge should exist\n",
" def decode(self, H, edge_index):\n",
" return (H[edge_index[0]] * H[edge_index[1]]).sum(dim=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train validation test split"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"e:\\DataScience\\Python_et_al\\Anaconda3_files\\Lib\\site-packages\\torch_geometric\\utils\\convert.py:249: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\b\\abs_abjetg6_iu\\croot\\pytorch_1686932924616\\work\\torch\\csrc\\utils\\tensor_new.cpp:248.)\n",
" data[key] = torch.tensor(value)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data(x=[7331, 768], edge_index=[2, 129408], edge_label=[258816], edge_label_index=[2, 258816], pos_edge_index=[2, 129408], neg_edge_index=[2, 129408])\n",
"tensor([[-3.5481, -2.8705, 1.3853, ..., -1.8805, 1.3289, -3.0499],\n",
" [ 0.0331, -0.8847, 1.8037, ..., 2.2501, 1.0772, -2.1120],\n",
" [-3.0940, -3.6991, 0.2336, ..., 1.3828, -1.6356, -2.8298],\n",
" ...,\n",
" [-3.8188, -4.7934, 3.2073, ..., -4.0812, 1.5829, -4.9556],\n",
" [-3.8116, -1.1899, 1.0038, ..., 1.9013, -2.8912, 2.1470],\n",
" [-0.4212, -1.6887, 1.3222, ..., 0.0060, -1.8694, -1.5078]])\n",
"\n",
"Train set: 129408 positive edges, 129408 negative edges\n",
"Val set: 16175 positive edges, 16175 negative edges\n",
"Test set: 16175 positive edges, 16175 negative edges\n"
]
}
],
"source": [
"def train_val_test_split(G: nx.Graph, val_ratio: float, test_ratio: float, neg_samp_ratio: float = 1.0) -> dict:\n",
" \"\"\"\n",
" Split the graph into train, validation and test sets\n",
" ## Parameters\n",
" G : networkx.Graph\n",
" The graph to split\n",
" val_ratio : float\n",
" The ratio of edges to use for the validation set\n",
" test_ratio : float\n",
" The ratio of edges to use for the test set\n",
" neg_samp_ratio : float (default=1.0)\n",
" The ratio of negative edges to sample for each positive edge\n",
" ## Returns\n",
" data : dict\n",
" A dictionary containing the train, validation and test sets\n",
" \n",
" \"\"\"\n",
"\n",
" data = from_networkx(G)\n",
" # data.train_mask = data.val_mask = data.test_mask = data.y = None\n",
" # data = train_test_split_edges(data, val_ratio, test_ratio)\n",
" \n",
" transform = RandomLinkSplit(\n",
" num_val = val_ratio,\n",
" num_test = test_ratio,\n",
" is_undirected = False,\n",
" add_negative_train_samples = True,\n",
" neg_sampling_ratio = neg_samp_ratio\n",
" )\n",
" train_data, val_data, test_data = transform(data)\n",
" \n",
" # create positive and negative edge index for each set\n",
" for d in [train_data, val_data, test_data]:\n",
" d.pos_edge_index = d.edge_label_index[:, d.edge_label==1]\n",
" d.neg_edge_index = d.edge_label_index[:, d.edge_label==0]\n",
" \n",
" data = {\n",
" 'train': train_data,\n",
" 'val': val_data,\n",
" 'test': test_data\n",
" }\n",
" return data\n",
"\n",
"\n",
"data = train_val_test_split(G_cite, 0.1, 0.1)\n",
"print(data['train'])\n",
"print(data['train'].x)\n",
"print()\n",
"print(f\"Train set: {data['train'].pos_edge_index.shape[1]} positive edges, {data['train'].neg_edge_index.shape[1]} negative edges\")\n",
"print(f\"Val set: {data['val'].pos_edge_index.shape[1]} positive edges, {data['val'].neg_edge_index.shape[1]} negative edges\")\n",
"print(f\"Test set: {data['test'].pos_edge_index.shape[1]} positive edges, {data['test'].neg_edge_index.shape[1]} negative edges\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# def train_val_test_split(G, val_ratio, test_ratio):\n",
"# \"\"\"\n",
"# Split the graph into train, validation and test sets\n",
"# ## Parameters\n",
"# G : networkx.Graph\n",
"# The graph to split\n",
"# val_ratio : float\n",
"# The ratio of edges to use for the validation set\n",
"# test_ratio : float\n",
"# The ratio of edges to use for the test set\n",
"# ## Returns\n",
"# data : torch_geometric.data.Data\n",
"# The data object containing the train, validation and test sets\n",
"# \"\"\"\n",
"\n",
"# data = from_networkx(G)\n",
"# data.train_mask = data.val_mask = data.test_mask = data.y = None\n",
"# data = train_test_split_edges(data, val_ratio, test_ratio)\n",
"\n",
"# return data\n",
"\n",
"\n",
"# data = train_val_test_split(G_cite, 0.1, 0.1)\n",
"# print(data.x)\n",
"# print(data)\n",
"# print()\n",
"# print(f'Train set: {data.train_pos_edge_index.shape[1]} positive edges, we will sample the same number of negative edges at runtime')\n",
"# print(f'Val set: {data.val_pos_edge_index.shape[1]} positive edges, {data.val_neg_edge_index.shape[1]} negative edges')\n",
"# print(f'Test set: {data.test_pos_edge_index.shape[1]} positive edges, {data.test_neg_edge_index.shape[1]} negative edges')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utility functions"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"# A utility function to compute the ROC-AUC score on given edges\n",
"def get_roc_auc(model, H, edge_index, labels):\n",
" with torch.no_grad():\n",
" #H = model.encode(data.x, data.train_pos_edge_index)\n",
" z = model.decode(H, edge_index)\n",
" s = z.sigmoid()\n",
" return roc_auc_score(labels, s)\n",
" \n",
"# A utility function to compute the F1-score on given edges\n",
"def get_f1_score(model, H, edge_index, labels):\n",
" with torch.no_grad():\n",
" z = model.decode(H, edge_index)\n",
" s = z.sigmoid().numpy() >= 0.5\n",
" return f1_score(labels, s)\n",
" \n",
"# A utility function to compute the confusion matrix on given edges\n",
"def get_confusion_matrix(model, H, edge_index, labels):\n",
" with torch.no_grad():\n",
" z = model.decode(H, edge_index)\n",
" s = z.sigmoid().numpy() >= 0.5\n",
" return confusion_matrix(labels, s) \n",
" \n",
"\n",
"# A utility function to plot all metrics\n",
"def plot_results(num_epochs, loss_all, auc_val_all, auc_test_all, f1_val_all, f1_test_all, auc_train_all, f1_train_all, figsize=(8,8)):\n",
" fig = plt.figure(figsize=figsize)\n",
" gs = GridSpec(3, 2, figure=fig)\n",
" loss_cut = 25\n",
"\n",
" # create sub plots\n",
" ax1 = fig.add_subplot(gs[0, 0])\n",
" ax2 = fig.add_subplot(gs[0, 1])\n",
" ax3 = fig.add_subplot(gs[1, :])\n",
" ax4 = fig.add_subplot(gs[2, :])\n",
" \n",
" # Plot loss for 10 epochs\n",
" ax1.set_title(f'Loss {loss_cut} epoch')\n",
" ax1.plot(np.arange(loss_cut)+1, loss_all[:loss_cut])\n",
" ax1.set_xlabel('Epoch')\n",
" \n",
" # Plot loss for remaining epochs\n",
" ax2.set_title(f'Loss after {loss_cut} epochs')\n",
" ax2.plot(np.arange(loss_cut, num_epochs)+1, loss_all[loss_cut:])\n",
" ax2.set_xlabel('Epoch')\n",
" \n",
" # Plot ROC AUC for remaining epochs\n",
" ax3.set_title('ROC AUC Scores')\n",
" ax3.plot(np.arange(num_epochs)+1, auc_train_all, label='ROC Train', alpha=0.8)\n",
" ax3.plot(np.arange(num_epochs)+1, auc_val_all, label='ROC Validation', alpha=0.8)\n",
" ax3.plot(np.arange(num_epochs)+1, auc_test_all, label='ROC Test', alpha=0.8)\n",
" ax3.set_xlabel('Epoch')\n",
" ax3.legend()\n",
"\n",
" # Plot ROC AUC for remaining epochs\n",
" ax4.set_title('F1-Scores')\n",
" ax4.plot(np.arange(num_epochs)+1, f1_train_all, label='F1 Train', alpha=0.8)\n",
" ax4.plot(np.arange(num_epochs)+1, f1_val_all, label='F1 Validation', alpha=0.8)\n",
" ax4.plot(np.arange(num_epochs)+1, f1_test_all, label='F1 Test', alpha=0.8)\n",
" ax4.set_xlabel('Epoch')\n",
" ax4.legend()\n",
"\n",
" fig.tight_layout()\n",
"\n",
"\n",
"def display_cms(cm_val, cm_test, title1, title2, figsize=(5,3)):\n",
" f, axes = plt.subplots(1,2, figsize=figsize)\n",
"\n",
" axes[0].set_title(title1, fontsize=10)\n",
" axes[0].grid(False)\n",
" ConfusionMatrixDisplay(cm_val).plot(colorbar=False, ax=axes[0])\n",
"\n",
" axes[1].set_title(title2, fontsize=10)\n",
" axes[1].grid(False)\n",
" ConfusionMatrixDisplay(cm_test).plot(colorbar=False, ax=axes[1])\n",
"\n",
" f.tight_layout()\n",
" plt.show()\n",
"\n",
"\n",
"def get_binary_metrics(model, H, edge_index, labels, auc_all: list, f1_all: list):\n",
" auc = get_roc_auc(model, H, edge_index, labels)\n",
" f1 = get_f1_score(model, H, edge_index, labels)\n",
" cm = get_confusion_matrix(model, H, edge_index, labels)\n",
" auc_all.append(auc)\n",
" f1_all.append(f1)\n",
"\n",
" return auc, f1, cm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define training function"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"def train(model: LinkPredictionModel, data, num_epochs: int, test: bool=True, verbose: bool=True, plot: bool=True) -> torch.Tensor:\n",
" \"\"\"\n",
" Trains a given model on a given dataset for a specified number of epochs, evaluating\n",
" performance metrics on validation and test sets.\n",
"\n",
" ## Parameters:\n",
" model : LinkPredictionModel\n",
" The model to be trained.\n",
" data : dict of torch_geometric.data.data.Data \n",
" The dataset containing training, validation, and test data.\n",
" num_epochs : int\n",
" The number of epochs to train the model.\n",
" test : bool, default=True\n",
" Whether to evaluate the model on the test dataset.\n",
" verbose : bool, default=True\n",
" Whether to print detailed progress messages.\n",
" plot : bool, default=True\n",
" Whether to plot performance metrics and confusion matrices.\n",
"\n",
" ## Returns:\n",
" H : torch.Tensor \n",
" The final node representations learned by the model.\n",
" \"\"\"\n",
"\n",
" # Initialize containers for storing performance metrics over epochs\n",
" loss_all = []\n",
" auc_train_all = []\n",
" auc_val_all = []\n",
" auc_test_all = []\n",
" f1_train_all = []\n",
" f1_val_all = []\n",
" f1_test_all = []\n",
" \n",
" # Set up the loss function and optimizer for binary classification\n",
" loss_fn = nn.BCEWithLogitsLoss()\n",
" optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
"\n",
" # Prepare validation and test data: positive and negative edge indices and labels\n",
" val_pos, val_neg = data['val'].pos_edge_index, data['val'].neg_edge_index\n",
" val_edge_index = torch.cat([val_pos, val_neg], dim=1)\n",
" val_labels = torch.cat([torch.ones(val_pos.shape[1]), torch.zeros(val_neg.shape[1])])\n",
" \n",
" test_pos, test_neg = data['test'].pos_edge_index, data['test'].neg_edge_index\n",
" test_edge_index = torch.cat([test_pos, test_neg], dim=1)\n",
" test_labels = torch.cat([torch.ones(test_pos.shape[1]), torch.zeros(test_neg.shape[1])])\n",
"\n",
" # Track the best F1 score on validation data\n",
" f1_val_best = -1\n",
"\n",
" for epoch in range(num_epochs):\n",
" # Sample negative edges for training\n",
" train_pos = data['train'].pos_edge_index\n",
" train_neg = data['train'].neg_edge_index #negative_sampling(\n",
" # edge_index=pos_edge_index,\n",
" # num_nodes=data.num_nodes,\n",
" # num_neg_samples=pos_edge_index.shape[1]\n",
" # )\n",
" \n",
" # Zero gradients, encode graph data, and decode to get scores for edges\n",
" optimizer.zero_grad()\n",
" H = model.encode(data['train'].x, train_pos)\n",
" train_edge_index = torch.cat([train_pos, train_neg], dim=1)\n",
" z = model.decode(H, train_edge_index)\n",
"\n",
" # Compute loss and backpropagate\n",
" train_labels = torch.cat([torch.ones(train_pos.shape[1]),\n",
" torch.zeros(train_neg.shape[1])])\n",
" loss = loss_fn(z, train_labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" if test:\n",
" # Evaluate model on validation and test sets, update metrics\n",
" auc_train, f1_train, cm_train = get_binary_metrics(model, H, train_edge_index, train_labels, auc_train_all, f1_train_all)\n",
" auc_val, f1_val, cm_val = get_binary_metrics(model, H, val_edge_index, val_labels, auc_val_all, f1_val_all)\n",
" auc_test, f1_test, cm_test = get_binary_metrics(model, H, test_edge_index, test_labels, auc_test_all, f1_test_all)\n",
" loss_all.append(loss.item()) # Convert loss to Python float and store\n",
" \n",
" if verbose and (epoch+1) % (50) == 0:\n",
" # Print metrics every 50 epochs\n",
" print(f'\\n[Epoch {epoch+1}/{num_epochs}] Loss: {loss:.4f} | AUC Train {auc_train:.3f} | AUC Val: {auc_val:.3f} | AUC Test: {auc_test:.3f}')\n",
" print(f' | F1 Train: {f1_train:.3f} | F1 Val: {f1_val:.3f} | F1 Test: {f1_test:.3f}')\n",
" if plot:\n",
" # Optionally plot confusion matrices\n",
" display_cms(cm_val, cm_test, title1=f'Epoch {epoch+1} Validation CM', title2=f'Epoch {epoch+1} Test CM')\n",
"\n",
" # Update best metrics if current F1 score on validation is the highest\n",
" if f1_val > f1_val_best:\n",
" f1_train_best, auc_train_best = f1_train, auc_train\n",
" f1_val_best, auc_val_best, cm_val_best = f1_val, auc_val, cm_val\n",
" f1_test_best, auc_test_best, cm_test_best, epoch_best, loss_best = f1_test, auc_test, cm_test, epoch+1, loss\n",
" if verbose:\n",
" print(f'\\n[Epoch {epoch+1}/{num_epochs}] Loss: {loss:.4f} | AUC Train {auc_train:.3f} | AUC Val: {auc_val:.3f} | AUC Test: {auc_test:.3f}')\n",
" print(f' | F1 Train: {f1_train:.3f} | F1 Val: {f1_val:.3f} | F1 Test: {f1_test:.3f}')\n",
"\n",
"\n",
" if test and verbose:\n",
" # Print best overall metrics after training\n",
" print(f'\\nBest Epoch: {epoch_best} | Loss: {loss_best:.4f} | AUC Train {auc_train:.3f} | AUC Val: {auc_val_best:.3f} | AUC Test: {auc_test_best:.3f}')\n",
" print(f' | F1 Train: {f1_train:.3f} | F1 Val: {f1_val_best:.3f} | F1 Test: {f1_test_best:.3f}')\n",
"\n",
" if test and plot:\n",
" # Plot best confusion matrices and performance metrics over epochs\n",
" display_cms(cm_val_best, cm_test_best, title1='Best Validation CM', title2='Best Test CM')\n",
" plot_results(num_epochs, loss_all, auc_val_all, auc_test_all, f1_val_all, f1_test_all, auc_train_all, f1_train_all)\n",
" \n",
" return H\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# def train(model, data, num_epochs, test=True, verbose=True, plot=True):\n",
"# \"\"\"\n",
"# Trains a given model on a given dataset for a specified number of epochs, evaluating\n",
"# performance metrics on validation and test sets.\n",
"\n",
"# ## Parameters:\n",
"# model : LinkPredictionModel\n",
"# The model to be trained.\n",
"# data : torch_geometric.data.data.Data \n",
"# The dataset containing training, validation, and test data.\n",
"# num_epochs : int\n",
"# The number of epochs to train the model.\n",
"# test : bool, default=True\n",
"# Whether to evaluate the model on the test dataset.\n",
"# verbose : bool, default=True\n",
"# Whether to print detailed progress messages.\n",
"# plot : bool, default=True\n",
"# Whether to plot performance metrics and confusion matrices.\n",
"\n",
"# ## Returns:\n",
"# H : tensor \n",
"# The final node representations learned by the model.\n",
"# \"\"\"\n",
"\n",
"# # Initialize containers for storing performance metrics over epochs\n",
"# loss_all = []\n",
"# auc_val_all = []\n",
"# auc_test_all = []\n",
"# f1_val_all = []\n",
"# f1_test_all = []\n",
" \n",
"# # Set up the loss function and optimizer for binary classification\n",
"# loss_fn = nn.BCEWithLogitsLoss()\n",
"# optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
"\n",
"# # Prepare validation and test data: positive and negative edge indices and labels\n",
"# val_pos, val_neg = data.val_pos_edge_index, data.val_neg_edge_index\n",
"# val_edge_index = torch.cat([val_pos, val_neg], dim=1)\n",
"# val_labels = torch.cat([torch.ones(val_pos.shape[1]), torch.zeros(val_neg.shape[1])])\n",
" \n",
"# test_pos, test_neg = data.test_pos_edge_index, data.test_neg_edge_index\n",
"# test_edge_index = torch.cat([test_pos, test_neg], dim=1)\n",
"# test_labels = torch.cat([torch.ones(test_pos.shape[1]), torch.zeros(test_neg.shape[1])])\n",
"\n",
"# # Track the best F1 score on validation data\n",
"# f1_val_best = -1\n",
"\n",
"# for epoch in range(num_epochs):\n",
"# # Sample negative edges for training\n",
"# pos_edge_index = data.train_pos_edge_index\n",
"# neg_edge_index = negative_sampling(\n",
"# edge_index=pos_edge_index,\n",
"# num_nodes=data.num_nodes,\n",
"# num_neg_samples=pos_edge_index.shape[1]\n",
"# )\n",
" \n",
"# # Zero gradients, encode graph data, and decode to get scores for edges\n",
"# optimizer.zero_grad()\n",
"# H = model.encode(data.x, pos_edge_index)\n",
"# edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=1)\n",
"# z = model.decode(H, edge_index)\n",
"\n",
"# # Compute loss and backpropagate\n",
"# labels = torch.cat([torch.ones(pos_edge_index.shape[1]),\n",
"# torch.zeros(neg_edge_index.shape[1])])\n",
"# loss = loss_fn(z, labels)\n",
"# loss.backward()\n",
"# optimizer.step()\n",
" \n",
"# if test:\n",
"# # Evaluate model on validation and test sets, update metrics\n",
"# auc_val, f1_val, cm_val = get_binary_metrics(model, H, val_edge_index, val_labels, auc_val_all, f1_val_all)\n",
"# auc_test, f1_test, cm_test = get_binary_metrics(model, H, test_edge_index, test_labels, auc_test_all, f1_test_all)\n",
"# loss_all.append(loss.item()) # Convert loss to Python float and store\n",
" \n",
"# if verbose and (epoch) % (50-1) == 0:\n",
"# # Print metrics every 50 epochs\n",
"# print(f'[Epoch {epoch+1}/{num_epochs}] Loss: {loss:.4f} | AUC Val: {auc_val:.3f} | AUC Test: {auc_test:.3f}')\n",
"# print(f' | F1 Val: {f1_val:.3f} | F1 Test: {f1_test:.3f}')\n",
"# if plot:\n",
"# # Optionally plot confusion matrices\n",
"# display_cms(cm_val, cm_test, title1=f'Epoch {epoch+1} Validation CM', title2=f'Epoch {epoch+1} Test CM')\n",
"\n",
"# # Update best metrics if current F1 score on validation is the highest\n",
"# if f1_val > f1_val_best:\n",
"# f1_val_best, auc_val_best, cm_val_best = f1_val, auc_val, cm_val\n",
"# f1_test_best, auc_test_best, cm_test_best, epoch_best, loss_best = f1_test, auc_test, cm_test, epoch+1, loss\n",
"# if verbose:\n",
"# print(f'[Epoch {epoch+1}/{num_epochs}] Loss: {loss:.4f} | AUC Val: {auc_val:.3f} | AUC Test: {auc_test:.3f}')\n",
"# print(f' | F1 Val: {f1_val:.3f} | F1 Test: {f1_test:.3f}')\n",
"\n",
"\n",
"# if test and verbose:\n",
"# # Print best overall metrics after training\n",
"# print(f'\\nBest Epoch: {epoch_best} | Loss: {loss_best:.4f} | AUC Val: {auc_val_best:.3f} | AUC Test: {auc_test_best:.3f}')\n",
"# print(f' | F1 Val: {f1_val_best:.3f} | F1 Test: {f1_test_best:.3f}')\n",
"\n",
"# if test and plot:\n",
"# # Plot best confusion matrices and performance metrics over epochs\n",
"# display_cms(cm_val_best, cm_test_best, title1='Best Validation CM', title2='Best Test CM')\n",
"# plot_results(num_epochs, loss_all, auc_val_all, auc_test_all, f1_val_all, f1_test_all)\n",
" \n",
"# return H\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## Model training"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2-layer GCN, hidden=128, output=64"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinkPredictionModel(\n",
" (encoder): ModuleList(\n",
" (0): GCNConv(768, 128)\n",
" (1): ReLU()\n",
" (2): GCNConv(128, 64)\n",
" )\n",
")\n",
"\n",
"[Epoch 1/100] Loss: 950.3169 | AUC Train 0.500 | AUC Val: 0.500 | AUC Test: 0.500\n",
" | F1 Train: 0.667 | F1 Val: 0.667 | F1 Test: 0.667\n",
"\n",
"[Epoch 2/100] Loss: 290.8552 | AUC Train 0.506 | AUC Val: 0.506 | AUC Test: 0.505\n",
" | F1 Train: 0.668 | F1 Val: 0.668 | F1 Test: 0.668\n",
"\n",
"[Epoch 3/100] Loss: 146.4323 | AUC Train 0.545 | AUC Val: 0.543 | AUC Test: 0.544\n",
" | F1 Train: 0.680 | F1 Val: 0.679 | F1 Test: 0.679\n",
"\n",
"[Epoch 4/100] Loss: 114.4558 | AUC Train 0.570 | AUC Val: 0.567 | AUC Test: 0.567\n",
" | F1 Train: 0.688 | F1 Val: 0.687 | F1 Test: 0.686\n",
"\n",
"[Epoch 5/100] Loss: 93.1828 | AUC Train 0.584 | AUC Val: 0.580 | AUC Test: 0.580\n",
" | F1 Train: 0.691 | F1 Val: 0.690 | F1 Test: 0.689\n",
"\n",
"[Epoch 6/100] Loss: 70.8734 | AUC Train 0.602 | AUC Val: 0.596 | AUC Test: 0.598\n",
" | F1 Train: 0.695 | F1 Val: 0.694 | F1 Test: 0.694\n",
"\n",
"[Epoch 7/100] Loss: 51.7604 | AUC Train 0.629 | AUC Val: 0.624 | AUC Test: 0.623\n",
" | F1 Train: 0.702 | F1 Val: 0.700 | F1 Test: 0.700\n",
"\n",
"[Epoch 8/100] Loss: 38.9135 | AUC Train 0.656 | AUC Val: 0.650 | AUC Test: 0.651\n",
" | F1 Train: 0.709 | F1 Val: 0.707 | F1 Test: 0.707\n",
"\n",
"[Epoch 9/100] Loss: 30.2415 | AUC Train 0.683 | AUC Val: 0.674 | AUC Test: 0.674\n",
" | F1 Train: 0.716 | F1 Val: 0.712 | F1 Test: 0.712\n",
"\n",
"[Epoch 10/100] Loss: 23.8183 | AUC Train 0.709 | AUC Val: 0.699 | AUC Test: 0.698\n",
" | F1 Train: 0.722 | F1 Val: 0.717 | F1 Test: 0.717\n",
"\n",
"[Epoch 11/100] Loss: 19.1752 | AUC Train 0.732 | AUC Val: 0.719 | AUC Test: 0.720\n",
" | F1 Train: 0.726 | F1 Val: 0.721 | F1 Test: 0.721\n",
"\n",
"[Epoch 12/100] Loss: 16.0042 | AUC Train 0.751 | AUC Val: 0.736 | AUC Test: 0.736\n",
" | F1 Train: 0.729 | F1 Val: 0.723 | F1 Test: 0.723\n",
"\n",
"[Epoch 13/100] Loss: 13.6546 | AUC Train 0.767 | AUC Val: 0.748 | AUC Test: 0.748\n",
" | F1 Train: 0.730 | F1 Val: 0.724 | F1 Test: 0.724\n",
"\n",
"[Epoch 14/100] Loss: 11.6519 | AUC Train 0.781 | AUC Val: 0.760 | AUC Test: 0.759\n",
" | F1 Train: 0.732 | F1 Val: 0.725 | F1 Test: 0.724\n",
"\n",
"[Epoch 15/100] Loss: 9.9172 | AUC Train 0.795 | AUC Val: 0.771 | AUC Test: 0.770\n",
" | F1 Train: 0.733 | F1 Val: 0.725 | F1 Test: 0.726\n",
"\n",
"[Epoch 17/100] Loss: 7.3725 | AUC Train 0.815 | AUC Val: 0.789 | AUC Test: 0.787\n",
" | F1 Train: 0.734 | F1 Val: 0.726 | F1 Test: 0.725\n",
"\n",
"[Epoch 50/100] Loss: 0.6913 | AUC Train 0.770 | AUC Val: 0.721 | AUC Test: 0.719\n",
" | F1 Train: 0.722 | F1 Val: 0.691 | F1 Test: 0.690\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[Epoch 100/100] Loss: 0.6376 | AUC Train 0.757 | AUC Val: 0.707 | AUC Test: 0.705\n",
" | F1 Train: 0.713 | F1 Val: 0.682 | F1 Test: 0.680\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Best Epoch: 17 | Loss: 7.3725 | AUC Train 0.757 | AUC Val: 0.789 | AUC Test: 0.787\n",
" | F1 Train: 0.713 | F1 Val: 0.726 | F1 Test: 0.725\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tensor([[-0.2757, -0.2116, -0.4192, ..., 0.4675, -0.3421, 0.1389],\n",
" [ 0.5064, 0.1709, -0.4319, ..., 0.1892, 0.3675, 0.5188],\n",
" [-0.7891, 0.1496, -0.5093, ..., -0.9087, -0.3726, 0.2214],\n",
" ...,\n",
" [-0.3083, 1.0202, -0.3997, ..., -1.0111, -1.4491, -0.6253],\n",
" [-0.2551, -0.3460, 0.2369, ..., -0.3878, 0.0566, -0.1443],\n",
" [ 0.1230, -0.2467, 0.2368, ..., -0.4407, 0.0190, 0.0969]],\n",
" grad_fn=<AddBackward0>)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = LinkPredictionModel(torch_geometric.nn.GCNConv, data['train'].x.shape[1])\n",
"print(model)\n",
"train(model, data, num_epochs=100) #AUC Val: 0.869 | AUC Test: 0.877\n",
" #F1 Val: 0.730 | F1 Test: 0.731"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2-layer GCN, hidden=256, output=128"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinkPredictionModel(\n",
" (encoder): ModuleList(\n",
" (0): GCNConv(768, 256)\n",
" (1): ReLU()\n",
" (2): GCNConv(256, 128)\n",
" )\n",
")\n",
"[Epoch 1/100] Loss: 1980.8561 | AUC Val: 0.500 | AUC Test: 0.500\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 3/100] Loss: 571.5208 | AUC Val: 0.500 | AUC Test: 0.501\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 4/100] Loss: 329.6139 | AUC Val: 0.508 | AUC Test: 0.507\n",
" | F1 Val: 0.669 | F1 Test: 0.669\n",
"[Epoch 5/100] Loss: 221.0074 | AUC Val: 0.528 | AUC Test: 0.530\n",
" | F1 Val: 0.675 | F1 Test: 0.676\n",
"[Epoch 6/100] Loss: 180.2908 | AUC Val: 0.546 | AUC Test: 0.547\n",
" | F1 Val: 0.682 | F1 Test: 0.682\n",
"[Epoch 7/100] Loss: 161.1303 | AUC Val: 0.556 | AUC Test: 0.557\n",
" | F1 Val: 0.685 | F1 Test: 0.686\n",
"[Epoch 8/100] Loss: 140.6978 | AUC Val: 0.568 | AUC Test: 0.569\n",
" | F1 Val: 0.690 | F1 Test: 0.690\n",
"[Epoch 9/100] Loss: 115.0681 | AUC Val: 0.587 | AUC Test: 0.589\n",
" | F1 Val: 0.696 | F1 Test: 0.698\n",
"[Epoch 10/100] Loss: 90.2215 | AUC Val: 0.614 | AUC Test: 0.614\n",
" | F1 Val: 0.706 | F1 Test: 0.706\n",
"[Epoch 11/100] Loss: 70.2873 | AUC Val: 0.640 | AUC Test: 0.639\n",
" | F1 Val: 0.715 | F1 Test: 0.715\n",
"[Epoch 12/100] Loss: 55.5926 | AUC Val: 0.664 | AUC Test: 0.662\n",
" | F1 Val: 0.723 | F1 Test: 0.723\n",
"[Epoch 13/100] Loss: 44.6001 | AUC Val: 0.683 | AUC Test: 0.681\n",
" | F1 Val: 0.730 | F1 Test: 0.729\n",
"[Epoch 14/100] Loss: 36.4007 | AUC Val: 0.700 | AUC Test: 0.698\n",
" | F1 Val: 0.734 | F1 Test: 0.733\n",
"[Epoch 15/100] Loss: 30.8216 | AUC Val: 0.712 | AUC Test: 0.711\n",
" | F1 Val: 0.737 | F1 Test: 0.735\n",
"[Epoch 35/100] Loss: 4.8720 | AUC Val: 0.814 | AUC Test: 0.816\n",
" | F1 Val: 0.737 | F1 Test: 0.739\n",
"[Epoch 38/100] Loss: 4.0889 | AUC Val: 0.817 | AUC Test: 0.819\n",
" | F1 Val: 0.738 | F1 Test: 0.740\n",
"[Epoch 50/100] Loss: 2.5163 | AUC Val: 0.812 | AUC Test: 0.816\n",
" | F1 Val: 0.734 | F1 Test: 0.737\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 100/100] Loss: 1.2161 | AUC Val: 0.775 | AUC Test: 0.778\n",
" | F1 Val: 0.716 | F1 Test: 0.718\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Best Epoch: 38 | Loss: 4.0889 | AUC Val: 0.817 | AUC Test: 0.819\n",
" | F1 Val: 0.738 | F1 Test: 0.740\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tensor([[-0.3955, 0.0149, 0.1458, ..., 0.0143, -0.3818, 0.4414],\n",
" [ 0.3724, 0.1665, -0.5627, ..., 0.2871, 0.1185, -0.4832],\n",
" [-0.0179, -0.3100, 0.3634, ..., 0.2538, -0.0490, -0.1802],\n",
" ...,\n",
" [ 2.7563, -1.2131, -0.7287, ..., -1.6780, -0.1462, -0.3413],\n",
" [-0.7131, 0.2238, 0.3304, ..., -0.4448, 0.6222, 0.0762],\n",
" [ 0.2999, -0.2302, -0.2424, ..., 0.2981, -0.0214, 0.0271]],\n",
" grad_fn=<AddBackward0>)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = LinkPredictionModel(torch_geometric.nn.GCNConv,\n",
" data['train'].x.shape[1],\n",
" sz_hid=256,\n",
" sz_out=128)\n",
"print(model)\n",
"train(model, data, num_epochs=100) # AUC Val: 0.875 | AUC Test: 0.879\n",
" # F1 Val: 0.764 | F1 Test: 0.768"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3-layer GCN, hidden=256, output=128"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinkPredictionModel(\n",
" (encoder): ModuleList(\n",
" (0): GCNConv(768, 256)\n",
" (1): ReLU()\n",
" (2): GCNConv(256, 256)\n",
" (3): ReLU()\n",
" (4): GCNConv(256, 128)\n",
" )\n",
")\n",
"[Epoch 1/100] Loss: 122.2062 | AUC Val: 0.500 | AUC Test: 0.500\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 1/100] Loss: 122.2062 | AUC Val: 0.500 | AUC Test: 0.500\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 4/100] Loss: 8.4495 | AUC Val: 0.790 | AUC Test: 0.794\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 5/100] Loss: 5.2952 | AUC Val: 0.871 | AUC Test: 0.875\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 6/100] Loss: 3.3788 | AUC Val: 0.897 | AUC Test: 0.900\n",
" | F1 Val: 0.671 | F1 Test: 0.672\n",
"[Epoch 7/100] Loss: 2.3327 | AUC Val: 0.901 | AUC Test: 0.902\n",
" | F1 Val: 0.683 | F1 Test: 0.684\n",
"[Epoch 8/100] Loss: 1.9001 | AUC Val: 0.901 | AUC Test: 0.902\n",
" | F1 Val: 0.689 | F1 Test: 0.689\n",
"[Epoch 9/100] Loss: 1.5518 | AUC Val: 0.903 | AUC Test: 0.904\n",
" | F1 Val: 0.693 | F1 Test: 0.693\n",
"[Epoch 10/100] Loss: 1.1965 | AUC Val: 0.903 | AUC Test: 0.904\n",
" | F1 Val: 0.705 | F1 Test: 0.705\n",
"[Epoch 11/100] Loss: 0.9493 | AUC Val: 0.902 | AUC Test: 0.903\n",
" | F1 Val: 0.713 | F1 Test: 0.714\n",
"[Epoch 20/100] Loss: 0.5571 | AUC Val: 0.907 | AUC Test: 0.907\n",
" | F1 Val: 0.713 | F1 Test: 0.716\n",
"[Epoch 21/100] Loss: 0.5548 | AUC Val: 0.899 | AUC Test: 0.900\n",
" | F1 Val: 0.719 | F1 Test: 0.722\n",
"[Epoch 22/100] Loss: 0.5580 | AUC Val: 0.892 | AUC Test: 0.893\n",
" | F1 Val: 0.723 | F1 Test: 0.726\n",
"[Epoch 23/100] Loss: 0.5636 | AUC Val: 0.887 | AUC Test: 0.888\n",
" | F1 Val: 0.724 | F1 Test: 0.728\n",
"[Epoch 40/100] Loss: 0.5507 | AUC Val: 0.902 | AUC Test: 0.903\n",
" | F1 Val: 0.725 | F1 Test: 0.729\n",
"[Epoch 41/100] Loss: 0.5494 | AUC Val: 0.902 | AUC Test: 0.903\n",
" | F1 Val: 0.726 | F1 Test: 0.730\n",
"[Epoch 42/100] Loss: 0.5496 | AUC Val: 0.902 | AUC Test: 0.903\n",
" | F1 Val: 0.727 | F1 Test: 0.731\n",
"[Epoch 43/100] Loss: 0.5474 | AUC Val: 0.902 | AUC Test: 0.904\n",
" | F1 Val: 0.728 | F1 Test: 0.731\n",
"[Epoch 44/100] Loss: 0.5458 | AUC Val: 0.902 | AUC Test: 0.904\n",
" | F1 Val: 0.729 | F1 Test: 0.733\n",
"[Epoch 45/100] Loss: 0.5441 | AUC Val: 0.902 | AUC Test: 0.904\n",
" | F1 Val: 0.730 | F1 Test: 0.734\n",
"[Epoch 46/100] Loss: 0.5426 | AUC Val: 0.902 | AUC Test: 0.904\n",
" | F1 Val: 0.731 | F1 Test: 0.735\n",
"[Epoch 47/100] Loss: 0.5425 | AUC Val: 0.902 | AUC Test: 0.904\n",
" | F1 Val: 0.733 | F1 Test: 0.736\n",
"[Epoch 48/100] Loss: 0.5398 | AUC Val: 0.901 | AUC Test: 0.904\n",
" | F1 Val: 0.734 | F1 Test: 0.738\n",
"[Epoch 49/100] Loss: 0.5393 | AUC Val: 0.900 | AUC Test: 0.903\n",
" | F1 Val: 0.737 | F1 Test: 0.741\n",
"[Epoch 50/100] Loss: 0.5365 | AUC Val: 0.899 | AUC Test: 0.902\n",
" | F1 Val: 0.739 | F1 Test: 0.743\n"
]
},
{
"data": {
"image/png": 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37t2meQ8ePCiaN28u7O3ty/wI1i1HjhwRQ4cOFd7e3sLe3l4EBQWJKVOmiHPnzgkhzG8cK40t3jgmE+IBrshXEzKZDBERERg8eLC1QyEiieH7A1WFaj3cTUREJGUs0kRERBLF4W4iIiKJ4pk0ERGRRLFIExERSRSLNBERkUSxSBMREUnUI/MFG/O/3obE5Ht/fRtZln2vm9YOoVqq6+CNkOZjrB1GpXnzO+ZyVbPrlnH/TmRR/o7e+KDF6Pv2e2SKdGJyOk5fKvubYcjyNDnlfyQoUXklJqfj9GXmclVSZqdaOwQqA4e7iYiIJIpFmoiISKJYpImIiCSKRZqIiEiiWKSJiIgkikWaiIhIolikiYiIJIpFmoiISKJYpImIiCSKRZqIiEiiWKSJiIgkikWaiIhIolikiYiIJIpFmoiISKJYpImIiCSKRZqIiEiiWKSJiIgkikWaiIhIolikiYiIJIpFmoiISKJYpImIiCSKRZqIiEiiWKSJiIgkikWaiIhIolikiYiIJIpFmoiISKJYpImIiCSKRZqIiEiiWKSJiIgkikWaiIhIolikiYiIJIpFmoiISKJYpImIiCSKRZqIiEiiWKSJiIgkikWaiIhIolikiYiIJIpFmoiISKJYpImIiCTKztoB2LIJnY+hW+ME+HtnokinQMylWvi/Xe2RlOZm1i/AOwOzeh1C68DrkMkELqa44821PXEjyxkumkJMefoftA+6jJquecjMVyMyPgBf7n4CeUX2pmU0qJ2KWb0PoXGdVBiEDH/G1sPS7U+iQKus4q2WBrsfMqD8MdOsTbgrULi2bom+ys/TYLctB9qXPGAY6mrWLj9eANlNA6CRwdhIDd1kd4i6KgCA/EQB7Ocml7r+wmU+EA3sS51GtmfiU8fQrVECArwyUaRXIOZyLSz7vT2SbrqZ+hwN+bLUeT/b1R4/RLW8q1Vg2XPb0LH+ZcxZ2xuRpwNNUxrWTsWsHofQpE4qDEYZ9sTXw6c7q2cu2/94E+qfM8zajO4K5PwUaJqu3JsLeaoeUMpgCLJH4ThPGBqqTf2V27OgisyB4nwRZAUCWesCASeFaboiJh9O/7lW6vpzP/OFIVhd6jSpYJF+CK0DriP8cBPEXa0BhdyIaT3+xvIJWzDy81Eo1BUnXB2PLHzz4iZsPtoQX+15AnmFKgR4Z0CrL9713s758HbJw+c7OuBiqjtqu+XiPwP3wts5H/9Z2wsA4OWchxUTt+D3k4/hoy2d4GivxWt9D+C9oX+a+lRHRn8lij6sdbtBLivRRx6VB/npIghPRYlpxvoqGJ52hPC2A3KMUP6YCdVbySha7QcoZDA2VqNgjZ/ZPMrVGZAfL4QIVll8e8h6WgdcR/iRJoj9N5dndP8bK57fguErbudyr4/Hmc3zZNAlvDsoEnvi65VY3pj2MRClrMfLOQ9fjNuC3089hiXbinN5Tp8DCBn8J+atq565bPBXIW+hz+0Gxe08NtZRoWCaN4y1lJBpjbCPyILj29eQ850/hGtxTsuKBPRtHKFv4wh12M2Sy2+kQfaPAWZt6h/SYRedD0N96R9oS6JI79y5E5s3b0ZmZiZ8fX0xYcIENGrUyNph3dfs7/uZvX5/Yzf8/tZqNKqTiuOJxX9003v8jaizdbF8ZwdTv6sZLqb/X0jxwLw1vW9PS3fF/35vi/dH/AGF3AiDUY5ODZKgN8qxZEsnCFH8B7zkt074aeZ6+Hpk4Ur67bPDakUhAzzu8SecpodqxU0ULawF1bs3Skw29L39e0AtQDfeHeppVyG7oYfwUQLKu5avF1Acyod+oAsgK3lAQLaby7N+NM/lkE3d8Mfc1Wjkk4rjScW5fDPXwaxP14aJ+Cehjlk+A0D9mml4rkMMxn0zDLte/95sWqfgJOgNcny47XYuh27rhDVTq3EuKwBRRh7rujmb/i8AFEzxgmpXNuQJRTC0LP59aAe7FS8mJr/05Stl5svXC9gdzoO2v6tN5LHVr0lHRUUhLCwMQ4cORWhoKBo1aoRFixYhLS3N2qFVmJNaCwDIzi8ePpHJBDo2uIRLaW5YNn4Ldv4nDKte2ogujRLuu5y8IhUMxuJfj9LOAL1BbkpqACj690y8pf/1ytgUmyC7qoP62UuwH3cZykUpkF3X3Z5oFFAtSYVuuCtEQDnOeguNsNuVA2Mtu+Iz61LID+YD2UYYejqXOr26eyRzuaD0oVAPx3w8Vf8Sfj3e0KxdrdRh0fA/sGTbUyWKOgCoFAbo7s5lXfHfW6u61TOX5Vd1cB6bAOeJidB8mGyex3fSCai2Z0E4ymEMfPAzYLtDeZBlG6C1kTy2epHesmULnn76aXTv3t105O3l5YVdu3aV2l+n0yE/P9/0U1BQUMURl0Xg1WeicDyxFi6keAAAPBwL4Givw/jOx3HwnB9mhfVHZHwgljy7E60DSr9G4qopxORuR7HxSGNT2z8X68DTqQBjn4qGncIAZ3URpvc8DADwci7j6PERZ2xoD+0b3ihaVAu6V7wgyzDA/tXrQLYBAGC3LgtQAIbBLvdcjuK3bKgHJUIzKAnyfwqgXVyr+Ay6FHY7c2Bso4GoIYkBKMl5lHL5td5ROJ50O5fv1r/lGeRpldgTH2jW/lrvKMRcrom/zgSWOt+RhDrwcirA80/ezuUZ3f/NZafql8uGBmrkz6mJvP/6IH92Dcgz9HB6/Qpk/+YxANgdzoPL0AtwGXwB9psykbfQxzTU/SBUu7Khb+0A4W0b9wBY9d1Gr9fj4sWLGDx4sFl78+bNcebMmVLniYiIwPr1602vAwMDERoaWplhlsvc/vsRVOsmXvxmsKlNJiu+KvVXfADWRLUAAJxN9kJzv2QMbRuHY4k+ZstwtNdi6bhtSEhxxzd72pjaL6Z4IGRDN7z6TBRm9DwMo5Dhl4PNcDNHA4NR+sM1lcH4xO2zFBEIaBvbQz3hCux+z4WhuRp2m7JRuMLnvsNZhqedYGytgSxdD7v12VAtTEHR0tqA6q7j11Q95EcLoH2rRmVsjs17lHJ5Xt/9qF/zJiavHFxmn0GtzmB7TH3TvSUA0LlBIp4IvIoxX40oc76LqR54b1M3vNo7CjN7HIbRKMPaw82QlquBQVS/XNY/4Wj2Oq+RGs6Tk6DcnQ3tUPfiPi00yP0/P8iyjVDtyILD4mTkLvWFcKt4+ZKl6WF3LB/5/6l1/84SYdUinZ2dDaPRCFdX8+swrq6uyMzMLHWeIUOGoH///qbXMglcU3i93350bpSIKd8OQkq2k6k9M18NvUGOhFR3s/4Jqe4lhqkdVFosG78VBVol3vi5NwxG8yPFnTH1sTOmPjwc81GgU0IIYEzHGFzLuPeZYrWhlsMYoITsqq74/rFMA9RjL5smy4yA8pt02G3KRtH3d9wM5iiHcJRD1FFC21AN9bAkKA7kw9DNyWzxdrtyAGc5jB1KDmHSo5PLbzyzH50bJOLFVea5fKeWda8jwCsT/wnvYdb+ROBV+HpkI/I/K83al4zcheOXauGlsEEAgB0n62PHSfNcfq4DcxkAoJbD4K+C/JrOrM3oowJ8gIKGaji9kATVzmwUjSp9lONeVLuyIZwV0Ld3vH9niZDEuF1pyVlWwiqVSiiVUhmmEHij/350bZyAqd8NLJFkeoMCcVe94e+VadZe1ysT1zNvXw9xtC8u0DqDHK/92Mfs6Pxu6XnFRWJA69PQ6hU4fMHXcptjy7QC8ss66JuqYehRfHZ8J/u3kqHv7gRDr9LfeM3o7rovVwgoduVC38MJsLN+IZEyW87luX33o1vDBEwJG4hrmWUXzMGt4xF3zRvnbniZtYftb4VNx8xvkls3fR0+3fkk9p7xL7GcW7k8sFVxLh+6yFyGTkBxWQtDU03ZfQRK5mh5CAHV7mzoujvbVB5btUi7uLhALpeXONLOysoqcUQuRfMG7EPv5ufx+k99kF+kgue/15RyC1WmG7t+2NcSi0b9juOJtfHPxTroUP8yOjVIwtSVAwEUn0Evn7AFaqUe7/7cG072OjjZFx9FZuSpYRTFw64j2p1CzKWaKNAq0S7oCmb3PoT/29UOuYXS/whBZbD7+iaM7R2Krw9nGqD8ORPIN8LQ0wlwUUC4mI9ECDsZhLsCwq/4JjLZdR0Uf+XB0EYDuCqKh8HWZQEqGQxtzc+W5dGFkCfroe1jGzeaWIOt5/J/+u1Dn2bn8dqaPsjXlp7LQPEBdY/GF7F0V4cSy7iZ61DqzWLJWU5mRX9k21OIuVwT+Vol2tW7gld6HcLy3dUzl9XfpkHXzhFGbzvIMw2wX5sOWb4R2u7OQKER9mszoG/vCKO7ArIcA+y3ZEOepoeu0+2DbVm6HrIMg+nsW5GohdDIIWrYQTjf8XnpEwXFedzLtkYsrFqk7ezsUK9ePcTExKBt27am9piYGDzxxBNWjKx8hreLAwB89cJms/YFG7piy793fUbGB2Lx5s6Y0PkY5vQ7gEtpbpi3phdOJNUGADSsk4pmfikAgE2vrTFbzsCPx+D6v8ndxDcFU7ofgYNKh8RUdyza3Bnbo4MrdfukTJZmgGpxavGNYq4KGBvao+gzH4ia5TszEyoZ5KcKYReRBeQaIdwUMDZTF1+PdjMv8IodOTA0tjc95IRKsvVcHvFEcS5/M9E8l0M2dcVv0bfv4O7V9DxkMmDnyaAHXleTOil4qeu/uZzmjoW/dca2mOqZy7I0PRxCkyHLNkC4KmBooEbuUr/iPNYaobiihWphNmRZBggXBQzBauR9VAdG/9sHNKptWWYPRHGaexUAkP9qDeh63i7Iqp3Z0DdSw2hjeSwTQjzAuIHlREVFYfny5XjxxRcRHByM3bt3448//sCnn34Kb2/vci/nufd/xOlLKZUYKd1NM6r0p3FR5Qp2roOV7V+2dhglWCqXn134I05fZi5XJeXgVGuHUO00cPHBD0/Oum8/q1+TfvLJJ5GTk4MNGzYgIyMDfn5+ePPNNyuU1ERkfcxlIsuzepEGgN69e6N3797370hEksZcJrIsqz/MhIiIiErHIk1ERCRR5Rru/uKLL8q9QJlMhmnTpj1wQERUOZjHRLanXEU6Nja23AuUwlODiKgk5jGR7SlXkV6xYkVlx0FElYx5TGR7eE2aiIhIoh74I1jR0dGIi4tDdnY2hg8fDi8vL5w/fx41atSAi4ttPXaNqLpiHhNJW4WLdFFREZYsWYJTp06Z2nr16gUvLy/89ttv8PT0xLhx4ywaJBFZFvOYyDZUeLh7zZo1uHjxIubMmYPVq1ebTWvRogVOnjxpseCIqHIwj4lsQ4XPpA8dOoRRo0ahbdu2MBqNZtO8vLyQlpZmseCIqHIwj4lsQ4XPpLOzs+HrW/r3nspkMmi12ocOiogqF/OYyDZUuEh7eHjg0qVLpU5LSkpCjRo1HjooIqpczGMi21DhIt22bVtEREQgISHB1CaTyZCamoqtW7eiQ4eSX4ZORNLCPCayDRW+Jj1ixAicOnUKb731Fvz8/AAUP27wxo0b8PHxweDBgy0dIxFZGPOYyDZUuEhrNBp88MEH2LZtG44dO4ZatWrB3t4egwcPRr9+/aBSqSojTiKyIOYxkW14oIeZqFQqDB48mEfbRDaMeUwkfQ/8xDGtVouEhATk5OTA2dkZgYGBPPomsjHMYyJpe6AivWXLFmzYsAH5+fmmNo1Gg2HDhmHAgAEWC46IKg/zmEj6Klykt2/fjh9++AHNmzdHx44d4ebmhszMTOzfvx8//vgjFAoF+vbtWxmxEpGFMI+JbEOFi/S2bdvQqVMnzJw506y9a9euWLZsGbZv387kJpI45jGRbajw56TT09Px1FNPlTqtc+fOSE9Pf+igiKhyMY+JbEOFi7SPjw+ysrJKnZaZmYlatWo9dFBEVLmYx0S2ocJFesSIEVi3bl2JRwomJSUhPDwco0aNslhwRFQ5mMdEtqFc16RDQ0PNXhuNRsydOxd+fn6mG04uX74Md3d3REZGom3btpUSLBE9OOYxke0pV5G++2hbLpfD09MT+fn5po9veHp6ltqXiKSBeUxke8pVpFesWFHZcRBRJWMeE9meCl+TJiIioqrxwI8FBYq/OL60L4f38vJ6mMUSURViHhNJ1wMV6Q0bNmD79u3Iyckpdfovv/zyUEERUeVjHhNJX4WHu/fs2YNNmzbhmWeeAQAMGTIEQ4YMgaenJ2rXro2pU6daPEgisizmMZFtqHCR3rlzpymhAaBt27YYPXo0PvvsM2g0mjKPyolIOpjHRLahwkU6OTkZwcHBkMlkAAC9Xg+g+Ltp+/fvj927d1s2QiKyOOYxkW2ocJFWKBQAAJlMBo1GY/aMX2dnZz7zl8gGMI+JbEOFi3Tt2rWRlpYGAHjsscfwxx9/QK/Xw2g0Yvfu3fD29rZ4kERkWcxjIttQ4SLdqlUrxMfHAyi+2eTUqVOYOHEiJk6ciMOHD2PQoEEWD5KILIt5TGQbKvwRrOHDh5v+37RpU/z3v/9FVFQUAKB169Zo2rSp5aIjokrBPCayDQ/1MBMACAoKQlBQkCViISIrYR4TSRMfC0pERCRR5TqTXrBgQbkXKJPJ8O677z5wQERUOZjHRLanXEVaCGH6PGV5+lqDy68x8DieYJV1V1c7P4i2dgjVk11jAC9XeDZbyGMAcNsYAy/mcpXaGRJt7RCqH7vGAGbdv1t5lhUSEvKQ0RCRtTGPiWwPr0kTERFJFIs0ERGRRLFIExERSRSLNBERkUSxSBMREUkUizQREZFEPfBjQa9evYq4uDjk5OTg6aefhpubG9LT0+Hk5ASVSmXJGImokjCPiaStwkXaaDTiq6++QmRkpKmtZcuWcHNzw9dff43AwECMGjXKkjESkYUxj4lsQ4WHuzdu3Ij9+/fj+eefxyeffGI2rVWrVoiOjrZUbERUSZjHRLahwmfSkZGRGDZsGPr37w+j0Wg2rUaNGkhJSbFYcERUOZjHRLahwmfS6enpCA4OLnWaUqlEYWHhQwdFRJWLeUxkGypcpF1dXcs8yr527Ro8PDweOigiqlzMYyLbUOEi3apVK2zcuBHp6emmNplMhvz8fGzfvh1t2rSxaIBEZHnMYyLbUOFr0iNHjsTx48fx6quvokmTJgCANWvW4PLly1AoFBg+fLjFgyQiy2IeE9mGCp9Ju7m5YfHixejYsSMSEhIgl8uRlJSEli1b4oMPPoCTk1NlxElEFsQ8JrIND/QwEzc3N0yZMsXSsRBRFWIeE0kfHwtKREQkURU+k/7iiy/uOV0mk2HatGkPHBARVT7mMZFtqHCRjo2NLdGWm5uLwsJCODg4wNHR0SKBEVHlYR4T2YYKF+kVK1aU2n7q1Cl8++23eO211x46KCKqXMxjIttgsWvSTZs2RZ8+fbBq1SpLLZKIqhjzmEhaLHrjmK+vL86fP2/JRRJRFWMeE0mHRYt0XFwcXFxcLLlIIqpizGMi6ajwNen169eXaNPpdEhKSkJ0dDQGDhxokcCIqPIwj4lsQ4WLdHh4eMmF2NmhRo0aGDlyJJObyAYwj4lsQ4WL9C+//FIZcRBRFWIeE9mGCl2T1mq1+Pzzz3H69OnKioeIKhnzmMh2VKhIq1Qq/PPPPzAajZUVDxFVMuYxke2o8N3dAQEBuHz5cmXEQkRVhHlMZBsqXKTHjBmDzZs3Iy4urjLiIaIqwDwmsg3lunEsLi4O9erVg1qtxrfffovCwkIsWLAATk5OcHNzg0wmM/WVyWT46KOPKi1gInowzGMi21OuIr1gwQIsXLgQQUFBcHZ25oMOiGwQ85jI9lT4I1ghISGVEAYRVSXmMZFtsOhjQYmIiMhyWKSJiIgkqtzD3QsWLIBcXr6avnr16gcOiIgqD/OYyLaUu0g3adKEN5oQ2TjmMZFtKXeRHj58OIKCgiozFiKqZMxjItvCa9JEREQSxSJNREQkUSzSREREElWua9L87tnya9ouFyOmp6J+s3x41tIjZFIADu5wNU2fs/QSeo3KMJsn/qgDXhlQ3/Ta3VuHF965jtadc+DgZMTlC/ZYu6wG9m91q6rNkLxxbRvjxhVVifYB41Mxc/FVZKTa4buFPjj6lzPyshRo2j4XMz64gjr1tKa+2iIZvnnfB5Gb3FFUKEOrp3Ixc/EVePvoTH2uXLDHN//1QdwRR+h1MgQ0LMD4eclo2TG3SrbTkpjHFfOwuezspsfzryejdZdcePtokZ1uh6gdrli9pBbycxRVui1SZYk83vajJ/6McMf5kxrk5yqwIf4knFwNpa5PWyTDy/2CcTFOgy92ncFjTQsqbdsspcJPHLO0uLg4bN68GQkJCcjIyMDrr7+Otm3bWjusB6Z2MOJirBq71rrj3e+SSu1zZI8zPnnVz/Rar5OZTZ+7/BIcnQ0ImRCIrHQFug3JxFtfJmHWMypcOOVQqfHbimXbz8BouL3fEk+r8eboIHQakAUhgAWTAqGwEwhZdREOTkZs/Nob/xkVhG/+Og21Q/FXNH75Xh0c/t0Fb/4vES7uBnz9vg/eHVcP/7fzDBT/voe+M64efOsVIjT8POzVRkR84413xwUi7GA8PGrorbHpksVcNs9lj5o6eNbU45v3a+PSWTVq+Gox+8Mr8KypwwdTAio7fJtgiTwuLJDj8a7ZeLxrNlYu9rnn+r77wAeetXS4GKep1O2yJKsPdxcVFSEgIACTJk2ydigW8c+fLli9pDYObHcrs49OK0NGqtL0k5NpfqzUqE0+fl3phTPRDki+ZI81n9dEXpYCQc2kf9RXVdw8DfCooTf9HN7titoBRWjeIRdXL9oj/qgjZn14BQ1aFsAvqAgzF19BQb4cf0a4AQDysuXYucYDL757Da075yKoWQHmLU9C4mk1ju9zBgBk3VTgWoI9Rs5MQb3GhahTT4tJ86+jqECBpDNqK269NDGXzXM56YwG/30xAId/d8X1JHucOOCMsNDaaNczG3KFqIItkL6HzWMAGPpiKkbNSkHDNvn3XNeRPc44+pczXnz3aiVvlWVZvUi3atUKo0ePRrt27awdSpVp3iEXv8TE4rt98Xjlo8tw9dSZTY/92xFdBmbC2U0PmUygy6AMKO0FYqKcrBSxtOm0MuzZ4I7eo29CJit+DQAqe6Opj0IBKJUCsUeK9+G5GAfodXK06ZJj6uNZSw//hoWIO+IIAHDxMKBu/ULsDvdAYb4cBj2w9QdPuHvrUL85D5juxlwumct3c3QxID9Xbnb2SMUeJI/LKyPVDp+94Ye5y5Ngr7GtAySrD3dXlE6ng053OxFkMhk0GtsZuvjnT2fs2+KGG1eUqFVXi/Fzk7Ek/CJm9qkPnbb4mGnhVH/M/zIJ6+NiodcBRQVyvD85ANeT7K0cvTRF7XBFbrYCvUamAwD8ggpR01eLlYtr4+XQK1A7GLHxK2+kpyiRfqP4Tz49xQ5KlRHObubXrty9dMhILe4jkwGL115AyMRADK7fDDJ58f0CC3+6WOY1Lyq/6pDLd3J212PMKzew7QdPK0QrfQ+Sx+UhBPDxK3XR7/mbCG5RgOTLJa+BS5nNFemIiAisX7/e9DowMBChoaFWjKhi/trsbvp/0hkNzp1wwPd/x6Nt92zTsNqEedfh5GrAvJH1kJ1uhw59sjD/q0TMGRKExNO28yZWVXau8cAT3bLhWav4GrGdEnjn2wR8+lpdDG/cDHKFQKtOOXji6ez7LksIGSC79X9g+Zu+cPPS45OI81CpjdixxhPvjg/Esm1n4VmT16QfRnXI5VscnAz47/cJuHRWjR8/rVXFkdoGS+bxnX79zgv5OXKMmnWjMsKudDZXpIcMGYL+/fubXt/5RfW2KD1FiZQrStPdirX9izBo0k1M6doASWeLr3tejNOgWbs8DJxwE8v+42vNcCXnxhUlju9zxjvfJpi1129egP/tPoO8bDl0OhncPA2Y3a8+gpsXX7fyqKGHTitHTqbC7Gw686YdGj+eBwCI3u+Ev3e7YH38STg6G/9d7hUc29sIu9d5YNSslCraykfTo57Lt2gcDVj480UU5suxYHIADHrb3s7K8KB5XB7RB5xx+pgj+ge0MGuf+Uwwnh6agTc+v2SRbagsNleklUollEqltcOwGGd3Pbx9dKbhG3tNcTEwGs37GQyATG5b11Kqwq61nnDz0qNdj9KPrh1dinfk1YsqnDvhgPFvJAMA6jfPh53SiGN7ndFlYCYA4OYNOySdVuOFt68BKL7MAAB3fx+FXCZg5K/ioT3quQwUn0Ev/PkidFoZ3psQCF2R1W8DkqQHzePymP7fK5gw7/ZH3m4mK/HWmMfw1peJaNiq/MXeWmyuSEud2sEAn8DbR9K1/LSo16QAOZkK5GQo8PzrN7B/qyvSbyhR00+LiW9eR1a6HQ5sL/785eXzaly9qMLLS67gm/d9kJ2hwJN9stC6cy7eHRdorc2SJKMR2PWLB3qMSIfirr/kvb+5wtXTgBp1tEiIV+PLd33RoU8W2nQtvlHM0cWI3s+m4+sFPnBx18PZzYBv/uuDgIaFaNWpuE+jNnlwcjXgo5fr4rlXk2GvFtj+kyeSL6vQtnvFhtzI9jxsLmscDVi05iLsNUYsmRUABycDHJyKR22ybtrBaOQZNfBweQwU31+SkaLEtYTia80Jp9VwcDTCu44WLu4G1PDVAbh974Pasbjg+/hrzZ6JIFVWL9KFhYVITr59VJSSkoLExEQ4OTnBy8vLipE9mOAWBfhowwXT66kLis/Kdv3ijuVv+iKgYQF6DM+Ao4sB6Sl2OHHACYum+qMgr/hIz6CX4e3n62HyW9exYHUCNI5GXEtQ4eOX/XBkD7+96E7H9zoj5aoKvUenl5iWfkOJr0LqIDPNDh419OgxIh1jXjG/JjU15CoUCoGFUwOgLZCj5VM5WLD6oukz0q6eBiz8+QLCPqyNeSODYNDJ4N+gECGrEvBYk8Kq2ESbwlw2z+X6zQvQ6N+PBYUdPG227HFtG5X6EI/q6GHzeOv3XmbX+V8fUvwwmeKHzZRcpq2RCSGsOnAXGxuLBQsWlGjv0qULZsyYUe7lTGszF+ePJ9y/I1nMzmvR1g6herJrDLnXr9aOogTmsu1iLltBOfPY6mfSTZo0wbp166wdBhE9JOYykeXxLgYiIiKJYpEmIiKSKBZpIiIiiWKRJiIikigWaSIiIolikSYiIpIoFmkiIiKJYpEmIiKSKBZpIiIiiWKRJiIikigWaSIiIolikSYiIpIoFmkiIiKJYpEmIiKSKBZpIiIiiWKRJiIikigWaSIiIolikSYiIpIoFmkiIiKJYpEmIiKSKBZpIiIiiWKRJiIikigWaSIiIolikSYiIpIoFmkiIiKJYpEmIiKSKBZpIiIiiWKRJiIikigWaSIiIolikSYiIpIoFmkiIiKJYpEmIiKSKBZpIiIiiWKRJiIikigWaSIiIolikSYiIpIoFmkiIiKJYpEmIiKSKBZpIiIiibKzdgCW4tewjrVDqH7stNaOoHpS1LN2BJWKuWwFzOWqV848lgkhRCWHQkRERA+Aw91WVFBQgHnz5qGgoMDaoVQr3O9kafybso7qsN9ZpK1ICIGEhARwMKNqcb+TpfFvyjqqw35nkSYiIpIoFmkiIiKJYpG2IqVSieHDh0OpVFo7lGqF+50sjX9T1lEd9jvv7iYiIpIonkkTERFJFIs0ERGRRLFIExERSRSLNBERkUQ9Ms/utkU7d+7E5s2bkZmZCV9fX0yYMAGNGjWydliPtLi4OGzevBkJCQnIyMjA66+/jrZt21o7LLJhzOOqV53ymGfSVhIVFYWwsDAMHToUoaGhaNSoERYtWoS0tDRrh/ZIKyoqQkBAACZNmmTtUOgRwDy2juqUxzyTtpItW7bg6aefRvfu3QEAEyZMwIkTJ7Br1y6MGTPGytE9ulq1aoVWrVpZOwx6RDCPraM65THPpK1Ar9fj4sWLaNGihVl78+bNcebMGStFRUQVwTymqsAibQXZ2dkwGo1wdXU1a3d1dUVmZqZ1giKiCmEeU1VgkbYimUxWrjYiki7mMVUmFmkrcHFxgVwuL3G0nZWVVeKonIikiXlMVYFF2grs7OxQr149xMTEmLXHxMSgQYMGVoqKiCqCeUxVgXd3W0n//v2xfPly1KtXD8HBwdi9ezfS0tLQs2dPa4f2SCssLERycrLpdUpKChITE+Hk5AQvLy8rRka2iHlsHdUpj/ktWFZ06yEIGRkZ8PPzw/jx49G4cWNrh/VIi42NxYIFC0q0d+nSBTNmzLBCRGTrmMdVrzrlMYs0ERGRRPGaNBERkUSxSBMREUkUizQREZFEsUgTERFJFIs0ERGRRLFIExERSRSLNBERkUSxSBMREUkUi7SFREZGYuTIkaaf0aNHY+rUqfjiiy+Qnp5eJTHMmDEDK1asML2OjY3FyJEjERsbW6HlnDlzBuvWrUNeXp6lQ8SKFSvK9USgkJAQhISEPNA6ZsyYgQ8//PCB5r3XMu/ct/ToYi6XD3O5avDZ3RY2ffp0+Pj4QKvVIj4+Hps2bUJcXBw+/vhjqNXqKo0lMDAQH3zwAXx9fSs035kzZ7B+/Xp07doVjo6OlRQdkbQxl0kKWKQtzM/PD4899hgAoGnTpjAajdiwYQOOHDmCTp06lTpPUVER7O3tLR6Lg4MDgoODLb5couqAuUxSwCJdyerXrw8ASE1NBVA8RHTo0CEsXLgQ33//Pc6ePQs/Pz8sXLgQer0ev/76K/bt24eUlBRoNBq0adMGY8eOhYuLi2mZer0ea9euxV9//YWCggIEBgZi/PjxJdZ96yH07733Hpo0aWJqP3fuHDZs2ICzZ8+iqKgIHh4eaNOmDSZMmIB169Zh/fr1AICZM2ea5rlzGVFRUdi6dSsuXboEAGjYsCHGjBmDwMBAs/VHRkYiIiICqampqFmzJgYPHvxQ+zI8PBzHjx/H9evXYTQaUatWLfTu3RvdunWDTCYr0f/vv//GunXrcP36dbi7u6Nv377o27evWZ/8/HysX78ehw8fRnp6OlxcXNChQweMHj26ys+WSNqYy8xla2CRrmS3vk7t7sQMDQ1Fz549MXjwYBgMBhiNRixZsgTx8fEYNGgQgoODkZaWhnXr1iEkJAQffvghVCoVAOCrr77C3r17MWDAADRv3hyXLl3Cxx9/jIKCgvvGEx0djdDQUPj6+mLcuHHw8vJCamoqTpw4AQDo3r07cnNzsWPHDrz++utwc3MDANMw28aNG/HLL7+ga9euGDZsGPR6PTZv3ox3330XixcvNvWLjIzEF198gccffxzjxo1Dfn4+wsPDodPpIJc/2K0Qqamp6NGjh+mr6M6dO4eVK1ciPT0dw4cPN+ubmJiIsLAwjBgxAm5ubti3bx/CwsKg1+sxcOBAAMVnPSEhIbh58yaGDBkCf39/XL58GevWrcOlS5fwzjvvlPqGQdUTc5m5bA0s0hZmNBphMBig0+kQFxeHjRs3QqPR4PHHHzf1MRgMGD58OLp162ZqO3DgAKKjozFnzhy0a9fO1O7v748333wTkZGR6NWrF65evYq//voL/fr1w9ixYwEAzZs3h5ubG5YtW3bf+L777jt4eXlh4cKFpjcKAKZYPD09TYkTEBCAGjVqmPqkpaUhPDwcvXv3xqRJk0ztzZs3x+zZsxEeHo5XX30VRqMRa9asQWBgIN544w1TcjRs2BCzZ8+Gh4dHhfbpLdOnTzf932g0okmTJhBCYPv27Rg2bJhZEmZkZCA0NBQBAQEAgFatWiE7OxsbNmxA7969YW9vj+3btyMpKQmLFi0yDWs2a9YMHh4e+PTTTxEdHY1WrVo9UKxk+5jLzGUpYJG2sPnz55u9rlu3Ll544QXTUewtdyYvABw9ehSOjo5o06YNDAaDqT0gIABubm6IjY1Fr169THd33n1NrEOHDve9Y/HatWu4ceMGnn32WbOkLq8TJ07AYDCgS5cuZjEqlUo0btzYFNu1a9eQkZGB/v37myWbt7c3GjRoYBourKhTp04hIiIC58+fL3GmkZWVZbaPfX19TUl9y1NPPYWYmBgkJCSgYcOGOHr0KOrWrYuAgACz7WnZsiVkMhliY2NtNrHp4TGXmctSwCJtYTNnzkSdOnWgUCjg6uoKd3f3En3s7e3h4OBg1paVlYW8vDyMGTOm1OXm5OSY/Xv3G4VCoYCTk9M9Y8vOzgZQfIT9ILKysgAAb775ZqnTbyVxbm5uqTHeanuQxD5//jw++OADNGnSBC+99BI8PT1hZ2eHI0eOYOPGjdBqtSXWU9q6gdv7MCsrC8nJyXj22WdLXeetflQ9MZeZy1LAIm1hderUMQ23VISzszOcnZ3x1ltvlTpdo9GY+gFAZmam2VCTwWAwJVRZbl1Lu3nzZoXju3Pdr732Gry9vcvsd+sNJjMzs8S00trK48CBA1AoFJg3b57ZmcORI0dK7X+vdd/aDmdnZ6hUKkybNq3UZdzqR9UTc5m5LAUs0hLRpk0bREVFwWg0mu4iLU3jxo0BAPv27UO9evVM7QcPHjQb5imNj48PatasiT///BP9+/eHUqkstd+t9ruPaFu0aAGFQoEbN26gffv291yPu7s7Dhw4YDZMlpqaijNnzjzQdSyZTAaFQmF2o4pWq8XevXtL7X/lyhUkJiaaDZPt378fGo3GdOdqmzZtEBERAWdnZ7PrdUQPg7l8b8zlimGRloiOHTti//79WLx4Mfr27YugoCAoFArcvHkTsbGxeOKJJ9C2bVv4+vqiU6dO2LZtGxQKhemO0N9++810hH4vkydPRmhoKObPn49+/frBy8sLaWlpOHHiBGbPng2g+NobAGzbtg1du3aFQqGAj48PatSogZEjR2Lt2rW4ceMGWrZsCScnJ2RmZuL8+fNQq9UYOXIk5HI5Ro0ahS+//BIfffQRevTogby8PISHh5c6dFUerVu3xpYtW7Bs2TL06NEDOTk5+O2338p8c3J3d8eSJUswYsQIuLu7Y+/evYiJicFzzz1n+hxr3759cfjwYbz33nvo168f6tatCyGEaX8MGDDgnm+yRKVhLt8bc7liWKQlQi6XY+7cudi2bRv27t2LiIgIKBQKeHp6olGjRqZkA4Bp06bB1dUVf/31F7Zv346AgADMmTMHn3/++X3X07JlSyxYsAAbNmzAqlWroNPp4OHhYXbHapMmTTB48GD89ddf+OOPPyCEMH22csiQIfD19cW2bdtw4MAB6PV6uLm54bHHHkPPnj1Ny3j66acBAL/++is+/vhjeHt7Y8iQIYiLi0NcXFyF90/Tpk0xbdo0/PrrrwgNDYWHhwe6d+8OFxcXfPnllyX6BwQEoGvXrggPDzd9tnLcuHHo37+/qY9arcaCBQuwadMm7N69GykpKVCpVPDy8kKzZs3uOQxIVBbm8r0xlytGJoQQ1g6CiIiISuIXbBAREUkUizQREZFEsUgTERFJFIs0ERGRRLFIExERSRSLNBERkUSxSBMREUkUizQREZFEsUgTERFJFIs0ERGRRLFIExERSdT/AxQscEUA2cpaAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 50/100] Loss: 0.5365 | AUC Val: 0.899 | AUC Test: 0.902\n",
" | F1 Val: 0.739 | F1 Test: 0.743\n",
"[Epoch 51/100] Loss: 0.5356 | AUC Val: 0.898 | AUC Test: 0.901\n",
" | F1 Val: 0.741 | F1 Test: 0.746\n",
"[Epoch 52/100] Loss: 0.5342 | AUC Val: 0.898 | AUC Test: 0.901\n",
" | F1 Val: 0.742 | F1 Test: 0.748\n",
"[Epoch 53/100] Loss: 0.5328 | AUC Val: 0.898 | AUC Test: 0.901\n",
" | F1 Val: 0.744 | F1 Test: 0.749\n",
"[Epoch 54/100] Loss: 0.5307 | AUC Val: 0.899 | AUC Test: 0.902\n",
" | F1 Val: 0.744 | F1 Test: 0.750\n",
"[Epoch 55/100] Loss: 0.5295 | AUC Val: 0.901 | AUC Test: 0.904\n",
" | F1 Val: 0.744 | F1 Test: 0.749\n",
"[Epoch 57/100] Loss: 0.5282 | AUC Val: 0.904 | AUC Test: 0.906\n",
" | F1 Val: 0.745 | F1 Test: 0.750\n",
"[Epoch 58/100] Loss: 0.5260 | AUC Val: 0.904 | AUC Test: 0.906\n",
" | F1 Val: 0.747 | F1 Test: 0.751\n",
"[Epoch 59/100] Loss: 0.5259 | AUC Val: 0.903 | AUC Test: 0.906\n",
" | F1 Val: 0.749 | F1 Test: 0.753\n",
"[Epoch 60/100] Loss: 0.5246 | AUC Val: 0.901 | AUC Test: 0.904\n",
" | F1 Val: 0.752 | F1 Test: 0.756\n",
"[Epoch 61/100] Loss: 0.5228 | AUC Val: 0.900 | AUC Test: 0.903\n",
" | F1 Val: 0.754 | F1 Test: 0.760\n",
"[Epoch 62/100] Loss: 0.5223 | AUC Val: 0.899 | AUC Test: 0.903\n",
" | F1 Val: 0.756 | F1 Test: 0.761\n",
"[Epoch 63/100] Loss: 0.5214 | AUC Val: 0.900 | AUC Test: 0.903\n",
" | F1 Val: 0.757 | F1 Test: 0.761\n",
"[Epoch 64/100] Loss: 0.5204 | AUC Val: 0.901 | AUC Test: 0.905\n",
" | F1 Val: 0.757 | F1 Test: 0.761\n",
"[Epoch 66/100] Loss: 0.5174 | AUC Val: 0.903 | AUC Test: 0.906\n",
" | F1 Val: 0.758 | F1 Test: 0.761\n",
"[Epoch 67/100] Loss: 0.5159 | AUC Val: 0.902 | AUC Test: 0.906\n",
" | F1 Val: 0.759 | F1 Test: 0.763\n",
"[Epoch 68/100] Loss: 0.5144 | AUC Val: 0.902 | AUC Test: 0.905\n",
" | F1 Val: 0.761 | F1 Test: 0.765\n",
"[Epoch 69/100] Loss: 0.5139 | AUC Val: 0.902 | AUC Test: 0.905\n",
" | F1 Val: 0.762 | F1 Test: 0.766\n",
"[Epoch 70/100] Loss: 0.5123 | AUC Val: 0.902 | AUC Test: 0.905\n",
" | F1 Val: 0.762 | F1 Test: 0.767\n",
"[Epoch 71/100] Loss: 0.5115 | AUC Val: 0.903 | AUC Test: 0.906\n",
" | F1 Val: 0.763 | F1 Test: 0.766\n",
"[Epoch 72/100] Loss: 0.5108 | AUC Val: 0.904 | AUC Test: 0.907\n",
" | F1 Val: 0.763 | F1 Test: 0.767\n",
"[Epoch 73/100] Loss: 0.5098 | AUC Val: 0.905 | AUC Test: 0.908\n",
" | F1 Val: 0.763 | F1 Test: 0.767\n",
"[Epoch 74/100] Loss: 0.5081 | AUC Val: 0.906 | AUC Test: 0.909\n",
" | F1 Val: 0.764 | F1 Test: 0.768\n",
"[Epoch 75/100] Loss: 0.5080 | AUC Val: 0.905 | AUC Test: 0.908\n",
" | F1 Val: 0.765 | F1 Test: 0.770\n",
"[Epoch 76/100] Loss: 0.5062 | AUC Val: 0.904 | AUC Test: 0.908\n",
" | F1 Val: 0.767 | F1 Test: 0.771\n",
"[Epoch 77/100] Loss: 0.5056 | AUC Val: 0.905 | AUC Test: 0.908\n",
" | F1 Val: 0.768 | F1 Test: 0.771\n",
"[Epoch 80/100] Loss: 0.5027 | AUC Val: 0.908 | AUC Test: 0.911\n",
" | F1 Val: 0.768 | F1 Test: 0.772\n",
"[Epoch 81/100] Loss: 0.5011 | AUC Val: 0.908 | AUC Test: 0.911\n",
" | F1 Val: 0.769 | F1 Test: 0.773\n",
"[Epoch 82/100] Loss: 0.5000 | AUC Val: 0.907 | AUC Test: 0.910\n",
" | F1 Val: 0.770 | F1 Test: 0.774\n",
"[Epoch 83/100] Loss: 0.4995 | AUC Val: 0.907 | AUC Test: 0.910\n",
" | F1 Val: 0.771 | F1 Test: 0.774\n",
"[Epoch 85/100] Loss: 0.4980 | AUC Val: 0.912 | AUC Test: 0.914\n",
" | F1 Val: 0.771 | F1 Test: 0.774\n",
"[Epoch 86/100] Loss: 0.4959 | AUC Val: 0.911 | AUC Test: 0.913\n",
" | F1 Val: 0.773 | F1 Test: 0.775\n",
"[Epoch 87/100] Loss: 0.4952 | AUC Val: 0.909 | AUC Test: 0.912\n",
" | F1 Val: 0.775 | F1 Test: 0.777\n",
"[Epoch 88/100] Loss: 0.4951 | AUC Val: 0.909 | AUC Test: 0.912\n",
" | F1 Val: 0.776 | F1 Test: 0.777\n",
"[Epoch 91/100] Loss: 0.4927 | AUC Val: 0.913 | AUC Test: 0.915\n",
" | F1 Val: 0.776 | F1 Test: 0.777\n",
"[Epoch 92/100] Loss: 0.4910 | AUC Val: 0.912 | AUC Test: 0.914\n",
" | F1 Val: 0.777 | F1 Test: 0.779\n",
"[Epoch 93/100] Loss: 0.4896 | AUC Val: 0.912 | AUC Test: 0.914\n",
" | F1 Val: 0.778 | F1 Test: 0.780\n",
"[Epoch 94/100] Loss: 0.4892 | AUC Val: 0.913 | AUC Test: 0.915\n",
" | F1 Val: 0.778 | F1 Test: 0.781\n",
"[Epoch 95/100] Loss: 0.4894 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.778 | F1 Test: 0.781\n",
"[Epoch 96/100] Loss: 0.4869 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.779 | F1 Test: 0.782\n",
"[Epoch 97/100] Loss: 0.4868 | AUC Val: 0.914 | AUC Test: 0.915\n",
" | F1 Val: 0.780 | F1 Test: 0.782\n",
"[Epoch 98/100] Loss: 0.4875 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.780 | F1 Test: 0.782\n",
"[Epoch 99/100] Loss: 0.4855 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.780 | F1 Test: 0.782\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 99/100] Loss: 0.4855 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.780 | F1 Test: 0.782\n",
"[Epoch 100/100] Loss: 0.4844 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.780 | F1 Test: 0.783\n",
"\n",
"Best Epoch: 100 | Loss: 0.4844 | AUC Val: 0.914 | AUC Test: 0.916\n",
" | F1 Val: 0.780 | F1 Test: 0.783\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tensor([[-0.1285, -0.0268, 0.0036, ..., 0.1564, 0.1560, 0.0947],\n",
" [ 0.2184, -0.0192, 0.2247, ..., -0.2015, -0.0713, -0.0999],\n",
" [-0.0047, -0.0154, -0.1481, ..., -0.0390, -0.1230, 0.1188],\n",
" ...,\n",
" [ 0.2457, -0.0358, -0.2312, ..., 0.1186, 0.3688, 0.0356],\n",
" [ 0.1251, 0.1499, -0.3413, ..., -0.0912, 0.3109, -0.3796],\n",
" [-0.1016, 0.0395, -0.0718, ..., -0.0550, 0.0326, 0.0123]],\n",
" grad_fn=<AddBackward0>)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = LinkPredictionModel(torch_geometric.nn.GCNConv,\n",
" data['train'].x.shape[1],\n",
" num_layers=3,\n",
" sz_hid=256,\n",
" sz_out=128)\n",
"print(model)\n",
"train(model, data, num_epochs=100) # AUC Val: 0.914 | AUC Test: 0.916\n",
" # F1 Val: 0.780 | F1 Test: 0.783"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4-layer GCN, hidden=256, output=128"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinkPredictionModel(\n",
" (encoder): ModuleList(\n",
" (0): GCNConv(768, 256)\n",
" (1): ReLU()\n",
" (2): GCNConv(256, 256)\n",
" (3): ReLU()\n",
" (4): GCNConv(256, 256)\n",
" (5): ReLU()\n",
" (6): GCNConv(256, 128)\n",
" )\n",
")\n",
"[Epoch 1/100] Loss: 42.9818 | AUC Val: 0.500 | AUC Test: 0.500\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 1/100] Loss: 42.9818 | AUC Val: 0.500 | AUC Test: 0.500\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 6/100] Loss: 1.2467 | AUC Val: 0.890 | AUC Test: 0.889\n",
" | F1 Val: 0.667 | F1 Test: 0.667\n",
"[Epoch 7/100] Loss: 0.9834 | AUC Val: 0.898 | AUC Test: 0.898\n",
" | F1 Val: 0.668 | F1 Test: 0.668\n",
"[Epoch 8/100] Loss: 0.8118 | AUC Val: 0.904 | AUC Test: 0.904\n",
" | F1 Val: 0.670 | F1 Test: 0.670\n",
"[Epoch 9/100] Loss: 0.7090 | AUC Val: 0.906 | AUC Test: 0.906\n",
" | F1 Val: 0.673 | F1 Test: 0.672\n",
"[Epoch 10/100] Loss: 0.6482 | AUC Val: 0.908 | AUC Test: 0.907\n",
" | F1 Val: 0.676 | F1 Test: 0.676\n",
"[Epoch 11/100] Loss: 0.6043 | AUC Val: 0.910 | AUC Test: 0.911\n",
" | F1 Val: 0.686 | F1 Test: 0.686\n",
"[Epoch 12/100] Loss: 0.5844 | AUC Val: 0.911 | AUC Test: 0.911\n",
" | F1 Val: 0.696 | F1 Test: 0.696\n",
"[Epoch 13/100] Loss: 0.5841 | AUC Val: 0.909 | AUC Test: 0.911\n",
" | F1 Val: 0.697 | F1 Test: 0.698\n",
"[Epoch 15/100] Loss: 0.5863 | AUC Val: 0.905 | AUC Test: 0.906\n",
" | F1 Val: 0.699 | F1 Test: 0.702\n",
"[Epoch 16/100] Loss: 0.5772 | AUC Val: 0.902 | AUC Test: 0.903\n",
" | F1 Val: 0.708 | F1 Test: 0.710\n",
"[Epoch 17/100] Loss: 0.5695 | AUC Val: 0.897 | AUC Test: 0.899\n",
" | F1 Val: 0.716 | F1 Test: 0.719\n",
"[Epoch 18/100] Loss: 0.5644 | AUC Val: 0.894 | AUC Test: 0.896\n",
" | F1 Val: 0.722 | F1 Test: 0.725\n",
"[Epoch 19/100] Loss: 0.5613 | AUC Val: 0.892 | AUC Test: 0.895\n",
" | F1 Val: 0.726 | F1 Test: 0.729\n",
"[Epoch 20/100] Loss: 0.5616 | AUC Val: 0.891 | AUC Test: 0.895\n",
" | F1 Val: 0.727 | F1 Test: 0.730\n",
"[Epoch 26/100] Loss: 0.5590 | AUC Val: 0.892 | AUC Test: 0.895\n",
" | F1 Val: 0.727 | F1 Test: 0.730\n",
"[Epoch 27/100] Loss: 0.5575 | AUC Val: 0.890 | AUC Test: 0.893\n",
" | F1 Val: 0.730 | F1 Test: 0.733\n",
"[Epoch 28/100] Loss: 0.5571 | AUC Val: 0.888 | AUC Test: 0.892\n",
" | F1 Val: 0.732 | F1 Test: 0.735\n",
"[Epoch 29/100] Loss: 0.5546 | AUC Val: 0.885 | AUC Test: 0.889\n",
" | F1 Val: 0.734 | F1 Test: 0.738\n",
"[Epoch 30/100] Loss: 0.5529 | AUC Val: 0.881 | AUC Test: 0.886\n",
" | F1 Val: 0.736 | F1 Test: 0.739\n",
"[Epoch 31/100] Loss: 0.5521 | AUC Val: 0.879 | AUC Test: 0.884\n",
" | F1 Val: 0.737 | F1 Test: 0.741\n",
"[Epoch 32/100] Loss: 0.5517 | AUC Val: 0.878 | AUC Test: 0.883\n",
" | F1 Val: 0.737 | F1 Test: 0.741\n",
"[Epoch 34/100] Loss: 0.5503 | AUC Val: 0.879 | AUC Test: 0.884\n",
" | F1 Val: 0.738 | F1 Test: 0.741\n",
"[Epoch 35/100] Loss: 0.5486 | AUC Val: 0.880 | AUC Test: 0.885\n",
" | F1 Val: 0.738 | F1 Test: 0.742\n",
"[Epoch 36/100] Loss: 0.5478 | AUC Val: 0.881 | AUC Test: 0.886\n",
" | F1 Val: 0.739 | F1 Test: 0.743\n",
"[Epoch 37/100] Loss: 0.5465 | AUC Val: 0.881 | AUC Test: 0.887\n",
" | F1 Val: 0.740 | F1 Test: 0.744\n",
"[Epoch 38/100] Loss: 0.5453 | AUC Val: 0.882 | AUC Test: 0.887\n",
" | F1 Val: 0.741 | F1 Test: 0.745\n",
"[Epoch 39/100] Loss: 0.5432 | AUC Val: 0.882 | AUC Test: 0.887\n",
" | F1 Val: 0.741 | F1 Test: 0.746\n",
"[Epoch 40/100] Loss: 0.5415 | AUC Val: 0.882 | AUC Test: 0.887\n",
" | F1 Val: 0.742 | F1 Test: 0.747\n",
"[Epoch 41/100] Loss: 0.5402 | AUC Val: 0.882 | AUC Test: 0.888\n",
" | F1 Val: 0.743 | F1 Test: 0.749\n",
"[Epoch 42/100] Loss: 0.5386 | AUC Val: 0.884 | AUC Test: 0.889\n",
" | F1 Val: 0.743 | F1 Test: 0.748\n",
"[Epoch 43/100] Loss: 0.5371 | AUC Val: 0.886 | AUC Test: 0.891\n",
" | F1 Val: 0.744 | F1 Test: 0.749\n",
"[Epoch 44/100] Loss: 0.5357 | AUC Val: 0.888 | AUC Test: 0.893\n",
" | F1 Val: 0.745 | F1 Test: 0.749\n",
"[Epoch 45/100] Loss: 0.5349 | AUC Val: 0.889 | AUC Test: 0.894\n",
" | F1 Val: 0.747 | F1 Test: 0.751\n",
"[Epoch 46/100] Loss: 0.5330 | AUC Val: 0.890 | AUC Test: 0.895\n",
" | F1 Val: 0.748 | F1 Test: 0.752\n",
"[Epoch 47/100] Loss: 0.5307 | AUC Val: 0.890 | AUC Test: 0.895\n",
" | F1 Val: 0.749 | F1 Test: 0.753\n",
"[Epoch 48/100] Loss: 0.5306 | AUC Val: 0.891 | AUC Test: 0.896\n",
" | F1 Val: 0.749 | F1 Test: 0.753\n",
"[Epoch 49/100] Loss: 0.5283 | AUC Val: 0.890 | AUC Test: 0.895\n",
" | F1 Val: 0.751 | F1 Test: 0.755\n",
"[Epoch 50/100] Loss: 0.5268 | AUC Val: 0.890 | AUC Test: 0.895\n",
" | F1 Val: 0.753 | F1 Test: 0.756\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 50/100] Loss: 0.5268 | AUC Val: 0.890 | AUC Test: 0.895\n",
" | F1 Val: 0.753 | F1 Test: 0.756\n",
"[Epoch 52/100] Loss: 0.5247 | AUC Val: 0.891 | AUC Test: 0.896\n",
" | F1 Val: 0.754 | F1 Test: 0.757\n",
"[Epoch 53/100] Loss: 0.5234 | AUC Val: 0.889 | AUC Test: 0.894\n",
" | F1 Val: 0.757 | F1 Test: 0.759\n",
"[Epoch 54/100] Loss: 0.5214 | AUC Val: 0.890 | AUC Test: 0.895\n",
" | F1 Val: 0.757 | F1 Test: 0.759\n",
"[Epoch 56/100] Loss: 0.5185 | AUC Val: 0.888 | AUC Test: 0.893\n",
" | F1 Val: 0.760 | F1 Test: 0.762\n",
"[Epoch 59/100] Loss: 0.5158 | AUC Val: 0.890 | AUC Test: 0.896\n",
" | F1 Val: 0.762 | F1 Test: 0.765\n",
"[Epoch 62/100] Loss: 0.5121 | AUC Val: 0.891 | AUC Test: 0.897\n",
" | F1 Val: 0.764 | F1 Test: 0.767\n",
"[Epoch 65/100] Loss: 0.5084 | AUC Val: 0.894 | AUC Test: 0.900\n",
" | F1 Val: 0.764 | F1 Test: 0.768\n",
"[Epoch 66/100] Loss: 0.5067 | AUC Val: 0.896 | AUC Test: 0.901\n",
" | F1 Val: 0.765 | F1 Test: 0.768\n",
"[Epoch 68/100] Loss: 0.5052 | AUC Val: 0.898 | AUC Test: 0.903\n",
" | F1 Val: 0.766 | F1 Test: 0.770\n",
"[Epoch 69/100] Loss: 0.5039 | AUC Val: 0.898 | AUC Test: 0.903\n",
" | F1 Val: 0.766 | F1 Test: 0.770\n",
"[Epoch 70/100] Loss: 0.5029 | AUC Val: 0.900 | AUC Test: 0.906\n",
" | F1 Val: 0.767 | F1 Test: 0.771\n",
"[Epoch 71/100] Loss: 0.5013 | AUC Val: 0.901 | AUC Test: 0.907\n",
" | F1 Val: 0.767 | F1 Test: 0.771\n",
"[Epoch 72/100] Loss: 0.4997 | AUC Val: 0.901 | AUC Test: 0.907\n",
" | F1 Val: 0.768 | F1 Test: 0.772\n",
"[Epoch 73/100] Loss: 0.4990 | AUC Val: 0.901 | AUC Test: 0.907\n",
" | F1 Val: 0.769 | F1 Test: 0.773\n",
"[Epoch 74/100] Loss: 0.4975 | AUC Val: 0.904 | AUC Test: 0.909\n",
" | F1 Val: 0.770 | F1 Test: 0.771\n",
"[Epoch 75/100] Loss: 0.4967 | AUC Val: 0.900 | AUC Test: 0.906\n",
" | F1 Val: 0.770 | F1 Test: 0.775\n",
"[Epoch 76/100] Loss: 0.4980 | AUC Val: 0.904 | AUC Test: 0.910\n",
" | F1 Val: 0.770 | F1 Test: 0.773\n",
"[Epoch 77/100] Loss: 0.4993 | AUC Val: 0.898 | AUC Test: 0.904\n",
" | F1 Val: 0.771 | F1 Test: 0.775\n",
"[Epoch 78/100] Loss: 0.4945 | AUC Val: 0.904 | AUC Test: 0.910\n",
" | F1 Val: 0.772 | F1 Test: 0.775\n",
"[Epoch 79/100] Loss: 0.4954 | AUC Val: 0.904 | AUC Test: 0.910\n",
" | F1 Val: 0.772 | F1 Test: 0.775\n",
"[Epoch 80/100] Loss: 0.4937 | AUC Val: 0.898 | AUC Test: 0.904\n",
" | F1 Val: 0.775 | F1 Test: 0.780\n",
"[Epoch 83/100] Loss: 0.4927 | AUC Val: 0.899 | AUC Test: 0.905\n",
" | F1 Val: 0.777 | F1 Test: 0.781\n",
"[Epoch 84/100] Loss: 0.4916 | AUC Val: 0.899 | AUC Test: 0.904\n",
" | F1 Val: 0.777 | F1 Test: 0.781\n",
"[Epoch 87/100] Loss: 0.4895 | AUC Val: 0.898 | AUC Test: 0.904\n",
" | F1 Val: 0.779 | F1 Test: 0.783\n",
"[Epoch 95/100] Loss: 0.4861 | AUC Val: 0.905 | AUC Test: 0.910\n",
" | F1 Val: 0.780 | F1 Test: 0.783\n",
"[Epoch 99/100] Loss: 0.4833 | AUC Val: 0.906 | AUC Test: 0.911\n",
" | F1 Val: 0.781 | F1 Test: 0.784\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 99/100] Loss: 0.4833 | AUC Val: 0.906 | AUC Test: 0.911\n",
" | F1 Val: 0.781 | F1 Test: 0.784\n",
"\n",
"Best Epoch: 99 | Loss: 0.4833 | AUC Val: 0.906 | AUC Test: 0.911\n",
" | F1 Val: 0.781 | F1 Test: 0.784\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tensor([[ 0.2209, 0.0011, -0.1834, ..., -0.0352, 0.0026, 0.1231],\n",
" [ 0.1435, -0.0497, -0.0685, ..., -0.1093, -0.0555, 0.0038],\n",
" [-0.1898, -0.0285, 0.0592, ..., 0.1724, 0.1460, -0.2407],\n",
" ...,\n",
" [-0.1194, -0.0399, 0.2202, ..., 0.0934, 0.0544, -0.0563],\n",
" [ 0.0813, 0.1628, -0.2378, ..., 0.1781, 0.3158, -0.0312],\n",
" [ 0.1022, -0.0210, -0.0925, ..., 0.0100, -0.0398, 0.0301]],\n",
" grad_fn=<AddBackward0>)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = LinkPredictionModel(torch_geometric.nn.GCNConv,\n",
" data['train'].x.shape[1],\n",
" num_layers=4,\n",
" sz_hid=256,\n",
" sz_out=128)\n",
"print(model)\n",
"train(model, data, num_epochs=100) # AUC Val: 0.906 | AUC Test: 0.911\n",
" # F1 Val: 0.781 | F1 Test: 0.784"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Citation prediction comparison"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"from scidocs.scidocs.user_activity_and_citations import make_run_from_embeddings, qrel_metrics\n",
"cit_pred_val_data = data_path + 'cite/val.qrel'\n",
"cit_pred_test_data = data_path + 'cite/test.qrel'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload SPECTER 2 embeddings"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"reading embeddings from file...: 100%|██████████| 4716/4716 [00:00<00:00, 428637.56it/s]\n"
]
}
],
"source": [
"sp2_embs = {}\n",
"with open('papers_embeddings_sp2.json', 'r') as f:\n",
" all_sp2_embs = json.load(f)\n",
" for id, emb in tqdm(all_sp2_embs.items(), desc='reading embeddings from file...'):\n",
" if id in G_cite.nodes:\n",
" sp2_embs[id] = np.array(emb, dtype=np.float32)\n",
" del all_sp2_embs\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train GNN"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinkPredictionModel(\n",
" (encoder): ModuleList(\n",
" (0): GCNConv(768, 256)\n",
" (1): ReLU()\n",
" (2): GCNConv(256, 256)\n",
" (3): ReLU()\n",
" (4): GCNConv(256, 256)\n",
" (5): ReLU()\n",
" (6): GCNConv(256, 128)\n",
" )\n",
")\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"e:\\DataScience\\Python_et_al\\Anaconda3_files\\Lib\\site-packages\\torch_geometric\\deprecation.py:22: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
" warnings.warn(out)\n"
]
}
],
"source": [
"gnn = LinkPredictionModel(torch_geometric.nn.GCNConv,\n",
" data['train'].x.shape[1],\n",
" num_layers=4,\n",
" sz_hid=256,\n",
" sz_out=128)\n",
"print(gnn)\n",
"data = train_val_test_split(G_cite, 0.0, 0.3, neg_samp_ratio=1.2)\n",
"H = train(gnn, data, num_epochs=100, test=False) \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison using scidocs functions\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the original dataset for values"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"\n",
"with open(cit_pred_test_data) as f_in:\n",
" qrels = [line.strip() for line in f_in]\n",
"\n",
"papers = defaultdict(list)\n",
"for line in qrels:\n",
" row = line.split(' ')\n",
" papers[row[0]].append(row[2])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"78"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(key in G_cite.nodes() for key in papers.keys())"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"reading embeddings from file...: 142009it [00:40, 3538.10it/s]\n"
]
}
],
"source": [
"embeddings = load_embeddings_from_jsonl(user_activity_and_citations_embeddings_path,\n",
" get_graph(paper_cite_file))"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'78495383450e02c5fe817e408726134b3084905d'"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(papers.keys())[0]"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"142009"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(\n",
" set(np.concatenate(list(papers.values()))).union(\n",
" set(embeddings.keys())\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000\n"
]
}
],
"source": [
"matching_keys = sum(key in embeddings.keys() for key in papers.keys())\n",
"print(matching_keys)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'map': 88.3, 'ndcg': 94.88}"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_path = 'cit_pred_test_sp1.qrel'\n",
"make_run_from_embeddings(\n",
" cit_pred_test_data,\n",
" embeddings,\n",
" results_path,\n",
" topk=5,\n",
" generate_random_embeddings=False\n",
" )\n",
"\n",
"cite_results = qrel_metrics(cit_pred_test_data, results_path, metrics=('ndcg', 'map'))\n",
"cite_results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create `.qrel` test dataset"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nodes with enough edges: 2031\n",
"pos_edges nodes len 1000 \n",
"pos_edges shape torch.Size([2, 8318]) \n",
"neg_edges nodes len 1000 \n",
"neg_edges shape torch.Size([2, 99816])\n",
"neg_edges[0] match tensor([[ 27, 227, 291, ..., 774, 116, 675],\n",
" [628, 462, 440, ..., 366, 777, 969]])\n",
"neg_edges[1] match tensor([[269, 377, 27, ..., 774, 116, 675],\n",
" [701, 475, 628, ..., 366, 777, 969]])\n"
]
}
],
"source": [
"# def generate_qrel_files(data, file_prefix):\n",
"# for split in ['test']: # 'val', \n",
"# pos_edge_index = getattr(data, f'{split}_pos_edge_index')\n",
"\n",
"# # Filter nodes with at least 5 positive edges\n",
"# nodes_with_enough_edges = pos_edge_index[0].unique(return_counts=True) # pos_edge_index.unique(return_counts=True)#\n",
"# nodes_with_enough_edges = nodes_with_enough_edges[0][nodes_with_enough_edges[1] >= 5] # topk will also be 2\n",
"# print('nodes with enough edges:', len(nodes_with_enough_edges))\n",
"\n",
"# pos_edge_index = pos_edge_index[:, torch.isin(pos_edge_index[0], nodes_with_enough_edges[:1000])]\n",
" \n",
"# # neg_edge_index = negative_sampling(\n",
"# # edge_index=pos_edge_index,\n",
"# # num_nodes=len(pos_edge_index[0].unique()), # data.num_nodes,\n",
"# # num_neg_samples=pos_edge_index.shape[1] * 12)\n",
"# neg_edge_index = getattr(data, f'{split}_neg_edge_index')\n",
"\n",
"# print(\n",
"# 'pos_edges nodes len', len(pos_edge_index[0].unique()),\n",
"# '\\npos_edges shape', pos_edge_index.shape,\n",
"# '\\nneg_edges nodes len', len(neg_edge_index[0].unique()),\n",
"# '\\nneg_edges shape', neg_edge_index.shape\n",
"# )\n",
"# print('neg_edges[0] match', neg_edge_index[:, torch.isin(neg_edge_index[0], nodes_with_enough_edges)])\n",
"# print('neg_edges[1] match', neg_edge_index[:, torch.isin(neg_edge_index[1], nodes_with_enough_edges)])\n",
"\n",
"# with open(f'{file_prefix}_{split}_nodes.qrel', 'w') as file:\n",
"# for node in pos_edge_index[0].unique():\n",
"# if neg_edge_index[:, neg_edge_index[0] == node].shape[1] < 25:\n",
"# continue\n",
"# # Positive edges for the node\n",
"# pos_edges = pos_edge_index[:, pos_edge_index[0] == node][:, :5]\n",
"# # Negative edges for the node\n",
"# neg_edges = neg_edge_index[:, neg_edge_index[0] == node][:, :25]\n",
"# #print(node, '->',neg_edges)\n",
" \n",
"# # Write to file\n",
"# for edge in pos_edges.T: # pos\n",
"# file.write(f\"{list(G_cite.nodes)[node.item()]} 0 {list(G_cite.nodes)[edge[1].item()]} 1\\n\")\n",
"# for edge in neg_edges.T: # neg\n",
"# file.write(f\"{list(G_cite.nodes)[node.item()]} 0 {list(G_cite.nodes)[edge[1].item()]} 0\\n\")\n",
"\n",
"# generate_qrel_files(data, 'new')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nodes with enough edges: 832\n",
"pos_edges nodes len 832 \n",
"pos_edges shape torch.Size([2, 5134]) \n",
"neg_edges nodes len 736 \n",
"neg_edges shape torch.Size([2, 1811])\n",
"neg_edges[0] match tensor([[7043, 4641, 3809, ..., 1764, 6416, 4773],\n",
" [3344, 4640, 981, ..., 1793, 2979, 4373]])\n",
"neg_edges[1] match tensor([[4641, 2150, 2044, 3826, 6288, 931, 2660, 5842, 4114, 857, 1480, 5000,\n",
" 1644, 2238, 1858, 2532, 4403, 4807, 5068, 1386, 1848, 2538, 2814, 6480,\n",
" 6587, 3871, 7043, 52, 1344, 3286, 1473, 4056, 4403, 1764, 2532, 7162,\n",
" 1421, 1791, 4004, 5946, 1433, 1644, 965, 1849, 2479, 5454, 1848, 6600,\n",
" 624, 2954, 817, 513, 612, 1054, 2344, 795, 5222, 6326, 3675, 822,\n",
" 4466, 3348, 3574, 6279, 498, 1839, 965, 3923, 6362, 6136, 6200, 2674,\n",
" 2199, 128, 4004, 6753, 125, 857, 517, 7303, 7226, 6132, 48, 1207,\n",
" 914, 1156, 702, 4180, 1240, 6302, 1229, 1454, 2428, 2969, 4261, 1123,\n",
" 517, 1386, 1848, 5904, 970, 3627, 4095, 2278, 52, 436, 1845, 6326,\n",
" 5454, 6132, 1404, 5000, 5372, 5767, 5184, 5509, 2521, 3702, 7144, 721,\n",
" 392, 1130, 1785, 5397, 4212, 4139, 3268, 7219, 7171, 1580, 3508, 4806,\n",
" 559, 2539, 3756, 1380, 454, 2347, 1961, 3146, 2498, 7234, 4224, 995,\n",
" 6600, 4467, 5545, 5545, 6416, 4038, 1955, 4512, 1114, 4641, 3907, 2512,\n",
" 3871, 6046, 3745, 5814, 1313, 3456, 6383, 7268, 2465, 1626, 3268, 6475,\n",
" 7125, 3263, 1024, 3268, 1836, 561, 5586, 6094, 7207, 5189, 3653, 4325,\n",
" 3408, 4962, 1112, 492, 2690, 6238, 3095, 4702, 5685, 2690, 6775, 1286,\n",
" 6475, 1764],\n",
" [4640, 6326, 44, 2347, 5780, 1062, 7301, 3167, 2538, 3052, 6277, 1616,\n",
" 2470, 3459, 3220, 576, 5139, 2771, 6220, 4891, 4567, 6302, 6838, 2355,\n",
" 6481, 5324, 7160, 5842, 2800, 827, 4794, 492, 593, 4315, 659, 1578,\n",
" 4794, 6095, 1313, 2420, 3082, 3311, 1421, 1687, 1251, 1138, 2915, 471,\n",
" 5037, 3112, 5216, 5571, 1836, 373, 2466, 2762, 3348, 4178, 3702, 3348,\n",
" 1001, 1537, 6679, 2363, 3082, 6794, 2721, 1388, 3190, 565, 52, 1786,\n",
" 436, 3076, 3727, 2630, 4262, 403, 6426, 1388, 994, 9, 6481, 1229,\n",
" 624, 5904, 4315, 37, 5221, 1834, 4977, 5349, 3073, 5534, 219, 4158,\n",
" 5649, 6136, 48, 471, 5028, 6412, 6528, 6726, 2708, 2980, 3328, 3328,\n",
" 1687, 3348, 4349, 1534, 7043, 3966, 6648, 637, 2915, 3606, 7010, 2365,\n",
" 1749, 6709, 2466, 4857, 4539, 4324, 2318, 5037, 1685, 1959, 4727, 5184,\n",
" 7018, 6700, 4524, 57, 1877, 8, 4128, 5053, 6587, 3537, 3192, 2870,\n",
" 917, 4497, 4559, 2504, 3127, 1787, 2415, 3702, 1277, 4942, 6046, 6778,\n",
" 6531, 493, 284, 4509, 4931, 2430, 3482, 79, 2208, 4584, 286, 4641,\n",
" 914, 3072, 5037, 4219, 3487, 276, 1172, 1369, 5722, 1720, 4460, 7057,\n",
" 2559, 5904, 5810, 5184, 5476, 497, 5153, 4349, 6444, 6709, 4128, 1656,\n",
" 5476, 1793]])\n"
]
}
],
"source": [
"def generate_qrel_files(data, file_prefix):\n",
" for split in ['test']: # 'val', \n",
" pos_edge_index = data[split].pos_edge_index\n",
" neg_edge_index = data[split].neg_edge_index\n",
"\n",
" # Filter nodes with at least 5 positive edges\n",
" nodes_with_enough_edges = pos_edge_index[0].unique(return_counts=True) # pos_edge_index.unique(return_counts=True)#\n",
" nodes_with_enough_edges = nodes_with_enough_edges[0][nodes_with_enough_edges[1] >= 5] # topk will also be 2\n",
" print('nodes with enough edges:', len(nodes_with_enough_edges))\n",
"\n",
" pos_edge_index = pos_edge_index[:, torch.isin(pos_edge_index[0], nodes_with_enough_edges[:1000])]\n",
" neg_edge_index = neg_edge_index[:, torch.isin(neg_edge_index[0], nodes_with_enough_edges[:1000])]\n",
"\n",
" print(\n",
" 'pos_edges nodes len', len(pos_edge_index[0].unique()),\n",
" '\\npos_edges shape', pos_edge_index.shape,\n",
" '\\nneg_edges nodes len', len(neg_edge_index[0].unique()),\n",
" '\\nneg_edges shape', neg_edge_index.shape\n",
" )\n",
" print('neg_edges[0] match', neg_edge_index[:, torch.isin(neg_edge_index[0], nodes_with_enough_edges)])\n",
" print('neg_edges[1] match', neg_edge_index[:, torch.isin(neg_edge_index[1], nodes_with_enough_edges)])\n",
" \n",
" with open(f'{file_prefix}_{split}_nodes.qrel', 'w') as file:\n",
" for node in pos_edge_index[0].unique():\n",
" if neg_edge_index[:, neg_edge_index[0] == node].shape[1] < 5:\n",
" continue\n",
" # Positive edges for the node\n",
" pos_edges = pos_edge_index[:, pos_edge_index[0] == node][:, :5]\n",
" # Negative edges for the node\n",
" neg_edges = neg_edge_index[:, neg_edge_index[0] == node][:, :25]\n",
" #print(node, '->',neg_edges)\n",
" \n",
" # Write to file\n",
" for edge in pos_edges.T: # pos\n",
" file.write(f\"{list(G_cite.nodes)[node.item()]} 0 {list(G_cite.nodes)[edge[1].item()]} 1\\n\")\n",
" for edge in neg_edges.T: # neg\n",
" file.write(f\"{list(G_cite.nodes)[node.item()]} 0 {list(G_cite.nodes)[edge[1].item()]} 0\\n\")\n",
"\n",
"generate_qrel_files(data, 'new')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'H' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[57], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m gnn_embeddings \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, node \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(G_cite\u001b[38;5;241m.\u001b[39mnodes):\n\u001b[1;32m----> 3\u001b[0m gnn_embeddings[node] \u001b[38;5;241m=\u001b[39m H[i]\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy()\n",
"\u001b[1;31mNameError\u001b[0m: name 'H' is not defined"
]
}
],
"source": [
"gnn_embeddings = {}\n",
"for i, node in enumerate(G_cite.nodes):\n",
" gnn_embeddings[node] = H[i].detach().numpy()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'map': 5.0, 'ndcg': 14.61}"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run_path = 'cit_pred_test_sp1.qrel'\n",
"make_run_from_embeddings(\n",
" 'new_test_nodes.qrel',\n",
" embeddings,\n",
" run_path,\n",
" topk=5,\n",
" generate_random_embeddings=False\n",
" )\n",
"\n",
"cite_results = qrel_metrics(cit_pred_test_data, run_path, metrics=('ndcg', 'map'))\n",
"cite_results\n",
"# {'map': 6.67, 'ndcg': 13.26}"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'gnn_embeddings' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[56], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m run_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcit_pred_test_gnn.qrel\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 2\u001b[0m make_run_from_embeddings(\n\u001b[0;32m 3\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnew_test_nodes.qrel\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m----> 4\u001b[0m gnn_embeddings,\u001b[38;5;66;03m#embeddings,\u001b[39;00m\n\u001b[0;32m 5\u001b[0m run_path,\n\u001b[0;32m 6\u001b[0m topk\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[0;32m 7\u001b[0m generate_random_embeddings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 8\u001b[0m )\n\u001b[0;32m 10\u001b[0m cite_results \u001b[38;5;241m=\u001b[39m qrel_metrics(cit_pred_test_data, run_path, metrics\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mndcg\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmap\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m 11\u001b[0m cite_results\n",
"\u001b[1;31mNameError\u001b[0m: name 'gnn_embeddings' is not defined"
]
}
],
"source": [
"run_path = 'cit_pred_test_gnn.qrel'\n",
"make_run_from_embeddings(\n",
" 'new_test_nodes.qrel',\n",
" gnn_embeddings,#embeddings,\n",
" run_path,\n",
" topk=5,\n",
" generate_random_embeddings=False\n",
" )\n",
"\n",
"cite_results = qrel_metrics(cit_pred_test_data, run_path, metrics=('ndcg', 'map'))\n",
"cite_results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recommendation comparison part (archived)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most code and comments are copied from https://github.com/allenai/scidocs/blob/master/scidocs/recomm_click_eval.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload propensity scores"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0.5929, 0.3906, 0.3019, 0.2665, 0.2509, 0.2073, 0.1840, 0.2287, 0.2125,\n",
" 0.2423])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recomm_path = data_path + 'recomm/'\n",
"propensity_score_path = recomm_path + 'propensity_scores.json'\n",
"\n",
"with open(propensity_score_path) as f_in:\n",
" adjClickDistribution = torch.Tensor(json.load(f_in)['scores'])\n",
"\n",
"adjClickDistribution"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([2.0604, 1.3574, 1.0491, 0.9261, 0.8719, 0.7204, 0.6394, 0.7948, 0.7385,\n",
" 0.8420])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# normalize so that average propensity score is equal to one (to keep loss on similar order as without adj):\n",
"adjClickDistribution = adjClickDistribution * len(adjClickDistribution) / (\n",
" sum(adjClickDistribution))\n",
"adjClickDistribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I've found what are those propencity scores in the SPECTER paper:\n",
"___\n",
"As is typical in clickthrough data on ranked lists, the clicks are biased toward the top of original ranking presented to the user. To counteract this effect, we computed `propensity scores` using a swap experiment (Agarwal et al., 2019). The `propensity scores` give, **for each position in the ranked list, the relative frequency that the position is over-represented in the data due to exposure bias.** We can then compute de-biased evaluation metrics by dividing the score for each test example by the propensity score for the clicked position\n",
"___"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload test or validation data (their metrics were achieved on **test** dataset)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pid</th>\n",
" <th>clicked_pid</th>\n",
" <th>similar_papers_avail_at_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>70fc27072f024415a812c2d819bfc6b50eefe326</td>\n",
" <td>65d3b7df2087134220369ac5c9f4f440ca538d08</td>\n",
" <td>5b2646605d804285176574c5f27f2974e83288e2,fee0c...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>f781499f9a2a81dfe50068d32bfe9f36449f1dc6</td>\n",
" <td>06c0db75dce988e17e27fb4f2269815c6075e8d8</td>\n",
" <td>562e2a1300e96cf882e17d7dee711d2dedac0580,06c0d...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>f8e8f26442a022b2a89b498d7e80701a86ff6691</td>\n",
" <td>2268a522cc5a749da38b4e9aaaa6f4239e82ca31</td>\n",
" <td>2268a522cc5a749da38b4e9aaaa6f4239e82ca31,0033a...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>92113b42ff8b9a09e45e7292b201e225bc89c379</td>\n",
" <td>171444e1456e3136ff4bec661eea5e75464d39b8</td>\n",
" <td>f3fdfc21ff5fb93625a65063f6f32b11d42a3218,17144...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>b7fcbb19c75ad65be522b64d5f4b23dbcb3b883b</td>\n",
" <td>90096a59f94b552351ad386db7bee8f1932badc8</td>\n",
" <td>08663b3b57c85d8270acf1931041050d26599c7e,90096...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pid \\\n",
"0 70fc27072f024415a812c2d819bfc6b50eefe326 \n",
"1 f781499f9a2a81dfe50068d32bfe9f36449f1dc6 \n",
"2 f8e8f26442a022b2a89b498d7e80701a86ff6691 \n",
"3 92113b42ff8b9a09e45e7292b201e225bc89c379 \n",
"4 b7fcbb19c75ad65be522b64d5f4b23dbcb3b883b \n",
"\n",
" clicked_pid \\\n",
"0 65d3b7df2087134220369ac5c9f4f440ca538d08 \n",
"1 06c0db75dce988e17e27fb4f2269815c6075e8d8 \n",
"2 2268a522cc5a749da38b4e9aaaa6f4239e82ca31 \n",
"3 171444e1456e3136ff4bec661eea5e75464d39b8 \n",
"4 90096a59f94b552351ad386db7bee8f1932badc8 \n",
"\n",
" similar_papers_avail_at_time \n",
"0 5b2646605d804285176574c5f27f2974e83288e2,fee0c... \n",
"1 562e2a1300e96cf882e17d7dee711d2dedac0580,06c0d... \n",
"2 2268a522cc5a749da38b4e9aaaa6f4239e82ca31,0033a... \n",
"3 f3fdfc21ff5fb93625a65063f6f32b11d42a3218,17144... \n",
"4 08663b3b57c85d8270acf1931041050d26599c7e,90096... "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data_path = recomm_path + 'test.csv'\n",
"val_data_path = recomm_path + 'val.csv'\n",
"\n",
"df_test = pd.read_csv(test_data_path)\n",
"\n",
"df_test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`similar_papers_avail_at_time` always have 10 papers inside divided by commas as one string"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 5b2646605d804285176574c5f27f2974e83288e2,fee0c...\n",
"1 562e2a1300e96cf882e17d7dee711d2dedac0580,06c0d...\n",
"2 2268a522cc5a749da38b4e9aaaa6f4239e82ca31,0033a...\n",
"3 f3fdfc21ff5fb93625a65063f6f32b11d42a3218,17144...\n",
"4 08663b3b57c85d8270acf1931041050d26599c7e,90096...\n",
" ... \n",
"995 2dbe0138ca07dbd9daa3d8d118c1ee2c49620b90,1c2a6...\n",
"996 577c4b3ad6ff6324878623c35f47ea4457d415d9,f3372...\n",
"997 7e5e5063cba7f4e41c2366eab80c1b2c6aaefa77,a6949...\n",
"998 0f31a74ec5cecab2314358bec4457738354bbc76,66f2c...\n",
"999 34b4a6ba45efe90c77b245890227cb35c598849e,784e3...\n",
"Name: similar_papers_avail_at_time, Length: 1000, dtype: object"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"count 1000.0\n",
"mean 10.0\n",
"std 0.0\n",
"min 10.0\n",
"25% 10.0\n",
"50% 10.0\n",
"75% 10.0\n",
"max 10.0\n",
"Name: similar_papers_avail_at_time, dtype: float64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"display(df_test['similar_papers_avail_at_time'])\n",
"\n",
"df_test['similar_papers_avail_at_time'].str.split(',').apply(len).describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the data consistency with our embeddings dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unique values in `val`/`test` dataset"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8758,)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(\n",
" np.concatenate([\n",
" df_test['pid'].unique(),\n",
" df_test['clicked_pid'].unique(),\n",
" np.concatenate(df_test['similar_papers_avail_at_time'].str.split(','))])\n",
" ).shape\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4716"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(G_cite.nodes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Intersection of `ids` in both datasets"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1104"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(np.concatenate([\n",
" df_test['pid'].unique(),\n",
" df_test['clicked_pid'].unique(),\n",
" np.concatenate(df_test['similar_papers_avail_at_time'].str.split(','))\n",
" ])).intersection(set(G_cite.nodes)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before it was 90, so the situation improved a little"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filter the dataset to have only available papers "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pid</th>\n",
" <th>clicked_pid</th>\n",
" <th>similar_papers_avail_at_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>b7fcbb19c75ad65be522b64d5f4b23dbcb3b883b</td>\n",
" <td>90096a59f94b552351ad386db7bee8f1932badc8</td>\n",
" <td>90096a59f94b552351ad386db7bee8f1932badc8,821fd...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>b7fcbb19c75ad65be522b64d5f4b23dbcb3b883b</td>\n",
" <td>821fd5bed14d6d06c25fbf44123fd7be382f7b4e</td>\n",
" <td>90096a59f94b552351ad386db7bee8f1932badc8,821fd...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>6dae703128d9caff2623eb8dfe2526dc6ad7aff5</td>\n",
" <td>4a63437aaee3267a5b427588adecb1c73a95b423</td>\n",
" <td>4a63437aaee3267a5b427588adecb1c73a95b423,0ce54...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>cb40a5e6d4fc0290452345791bb91040aed76961</td>\n",
" <td>c6a9ca56c93323c0199dd22631d1cf731bdd7ec1</td>\n",
" <td>8fd1d13e18c5ef8b57296adab6543cb810c36d81,5772b...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3d9d1c26a8dc82ca54154d6f2304507ce65bcb35</td>\n",
" <td>fc7d91dbb549a5c834d663944670bf4f08bc9b00</td>\n",
" <td>7874c01633ca800b6e38b188dd0b1cbf6f3f9ece,0dc6f...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>935</th>\n",
" <td>1e56ed3d2c855f848ffd91baa90f661772a279e1</td>\n",
" <td>3d74d241af53d85cbc74f73a785fddd675f0e644</td>\n",
" <td>3d74d241af53d85cbc74f73a785fddd675f0e644,9e6f3...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>937</th>\n",
" <td>cf755dd097b7286e8e53ba637c495229f2587e84</td>\n",
" <td>1b49a3b9a44c5213a03129faba81cccc75a4d5ff</td>\n",
" <td>1b49a3b9a44c5213a03129faba81cccc75a4d5ff,b946c...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>948</th>\n",
" <td>9a6a8cbb10b719cdb565ca9be01c7bf4df049bf0</td>\n",
" <td>560d3334047c0455951a4a672941103bf49c1f40</td>\n",
" <td>3e5655307333589f52f2aab8678f3e1c9184a88f,b77b4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>a217e85ca33c46ebb06812fdd30f133a83cd0890</td>\n",
" <td>3b3291e1f908f583a478e15fb40bffb6dd26cf14</td>\n",
" <td>3b3291e1f908f583a478e15fb40bffb6dd26cf14,ad66b...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>d4c5b8cdeedb5369bc8738f5ec14d8bc3dc02477</td>\n",
" <td>ae0ea06b05a19e11f5a85bdb1eb5553b8580ad96</td>\n",
" <td>ae0ea06b05a19e11f5a85bdb1eb5553b8580ad96,30611...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>71 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" pid \\\n",
"4 b7fcbb19c75ad65be522b64d5f4b23dbcb3b883b \n",
"6 b7fcbb19c75ad65be522b64d5f4b23dbcb3b883b \n",
"14 6dae703128d9caff2623eb8dfe2526dc6ad7aff5 \n",
"17 cb40a5e6d4fc0290452345791bb91040aed76961 \n",
"18 3d9d1c26a8dc82ca54154d6f2304507ce65bcb35 \n",
".. ... \n",
"935 1e56ed3d2c855f848ffd91baa90f661772a279e1 \n",
"937 cf755dd097b7286e8e53ba637c495229f2587e84 \n",
"948 9a6a8cbb10b719cdb565ca9be01c7bf4df049bf0 \n",
"985 a217e85ca33c46ebb06812fdd30f133a83cd0890 \n",
"991 d4c5b8cdeedb5369bc8738f5ec14d8bc3dc02477 \n",
"\n",
" clicked_pid \\\n",
"4 90096a59f94b552351ad386db7bee8f1932badc8 \n",
"6 821fd5bed14d6d06c25fbf44123fd7be382f7b4e \n",
"14 4a63437aaee3267a5b427588adecb1c73a95b423 \n",
"17 c6a9ca56c93323c0199dd22631d1cf731bdd7ec1 \n",
"18 fc7d91dbb549a5c834d663944670bf4f08bc9b00 \n",
".. ... \n",
"935 3d74d241af53d85cbc74f73a785fddd675f0e644 \n",
"937 1b49a3b9a44c5213a03129faba81cccc75a4d5ff \n",
"948 560d3334047c0455951a4a672941103bf49c1f40 \n",
"985 3b3291e1f908f583a478e15fb40bffb6dd26cf14 \n",
"991 ae0ea06b05a19e11f5a85bdb1eb5553b8580ad96 \n",
"\n",
" similar_papers_avail_at_time \n",
"4 90096a59f94b552351ad386db7bee8f1932badc8,821fd... \n",
"6 90096a59f94b552351ad386db7bee8f1932badc8,821fd... \n",
"14 4a63437aaee3267a5b427588adecb1c73a95b423,0ce54... \n",
"17 8fd1d13e18c5ef8b57296adab6543cb810c36d81,5772b... \n",
"18 7874c01633ca800b6e38b188dd0b1cbf6f3f9ece,0dc6f... \n",
".. ... \n",
"935 3d74d241af53d85cbc74f73a785fddd675f0e644,9e6f3... \n",
"937 1b49a3b9a44c5213a03129faba81cccc75a4d5ff,b946c... \n",
"948 3e5655307333589f52f2aab8678f3e1c9184a88f,b77b4... \n",
"985 3b3291e1f908f583a478e15fb40bffb6dd26cf14,ad66b... \n",
"991 ae0ea06b05a19e11f5a85bdb1eb5553b8580ad96,30611... \n",
"\n",
"[71 rows x 3 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create mask to filter only available papers in 'pid' and 'clicked_pid' cols\n",
"mask = (df_test['pid'].isin(G_cite.nodes)) & (df_test['clicked_pid'].isin(G_cite.nodes))\n",
"df_test = df_test[mask]\n",
"\n",
"# Split the 'sim_papers' col and leave values that are available in the Graph\n",
"df_test['similar_papers_avail_at_time'] = df_test['similar_papers_avail_at_time'].str.split(',')\n",
"df_test['similar_papers_avail_at_time'] = df_test['similar_papers_avail_at_time'].apply(\n",
" lambda ls: [paper for paper in ls if paper in G_cite.nodes]\n",
" )\n",
"\n",
"# Get rid of rows with less then 2 values (comparison won't make sence)\n",
"df_test = df_test[df_test['similar_papers_avail_at_time'].apply(len) > 1]\n",
"df_test['similar_papers_avail_at_time'] = df_test['similar_papers_avail_at_time'].apply(lambda lst: ','.join(lst))\n",
"display(df_test)\n",
"\n",
"# Upload filtered dataset\n",
"test_filtered_data_path = recomm_path + 'test_filtered.csv'\n",
"df_test.to_csv(test_filtered_data_path, index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Open the `csv` as it was in original code"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clicks variable contain 71 examples with columns:\n",
"1.pid\n",
"2.clicked_pid\n",
"3.similar_papers_avail_at_time\n"
]
}
],
"source": [
"with open(test_filtered_data_path, 'r') as f_in:\n",
" clicks = f_in.read().splitlines()\n",
"\n",
"print(f\"Clicks variable contain {len(clicks[1:])} examples with columns:\")\n",
"\n",
"for i, cl in enumerate(clicks[0].split(',')):\n",
" print(f'{i+1}.{cl}')\n",
"\n",
"clicks = clicks[1:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison for-loop (work in progress)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"reading embeddings from file...: 100%|██████████| 4716/4716 [00:00<00:00, 16254.41it/s]\n"
]
}
],
"source": [
"#sp2_embs = pd.read_json('papers_embeddings_sp2.json')\n",
"sp2_embs = {}\n",
"with open('papers_embeddings_sp2.json', 'r') as f:\n",
" all_sp2_embs = json.load(f)\n",
" for id, emb in tqdm(all_sp2_embs.items(), desc='reading embeddings from file...'):\n",
" if id in G_cite.nodes:\n",
" sp2_embs[id] = np.array(emb, dtype=np.float32)\n",
" del all_sp2_embs\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinkPredictionModel(\n",
" (encoder): ModuleList(\n",
" (0): GCNConv(768, 256)\n",
" (1): ReLU()\n",
" (2): GCNConv(256, 256)\n",
" (3): ReLU()\n",
" (4): GCNConv(256, 128)\n",
" )\n",
")\n"
]
}
],
"source": [
"gnn = LinkPredictionModel(torch_geometric.nn.GCNConv,\n",
" data.x.shape[1],\n",
" num_layers=3,\n",
" sz_hid=256,\n",
" sz_out=128)\n",
"print(gnn)\n",
"data = train_val_test_split(G_cite, 0,0)\n",
"H = train(gnn, data, num_epochs=100, test=False) # new embs AUC Val: 0.977 | AUC Test: 0.976 | F1 Val : 0.823 | F1 Test: 0.825\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for finding euclidean distance on SPECTER 2 embeddings"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"from scipy.spatial.distance import euclidean\n",
"\n",
"def find_L2_distance_SPECTER2(embs, query_paper_id, comp_paper_id):\n",
" \"\"\"\n",
" Computes the negative L2 (Euclidean) distance between the embeddings of two papers,\n",
" effectively turning the distance metric into a similarity metric where larger values indicate closer or more similar papers.\n",
"\n",
" Parameters:\n",
" embs (dict): the embeddings dictionary containing 'id': 'embedding_vector' pairs\n",
" query_paper_id (str): The identifier for the query paper. This ID should correspond to a key in the `sp2_embs` dictionary.\n",
" comp_paper_id (str): The identifier for the comparison paper. This ID should also correspond to a key in the `sp2_embs` dictionary.\n",
"\n",
" Returns:\n",
" float: The negative L2 distance between the embeddings of the query paper and the comparison paper.\n",
" \n",
" \"\"\"\n",
"\n",
" # Retrieve the embeddings for the query and comparison papers from the pre-loaded embeddings dictionary\n",
" query_emb = embs[query_paper_id]\n",
" comp_emb = embs[comp_paper_id]\n",
"\n",
" # Compute the Euclidean (L2) distance between the two embeddings\n",
" dist = euclidean(query_emb, comp_emb)\n",
"\n",
" # Return the negative distance to convert the distance metric into a similarity metric\n",
" # A larger (less negative) value indicates a smaller distance or higher similarity\n",
" return -dist\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for receiving dot-product between two paper nodes in GNN (in progress)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"def get_dot_prod_GNN(H, G, query_paper, comp_paper):\n",
" \"\"\"\n",
" Computes the dot product similarity between two nodes (papers) in a graph using a trained GNN.\n",
"\n",
" Parameters:\n",
" H (Tensor): The node embeddings matrix produced by the GNN, where each row corresponds to a node's embedding.\n",
" query_paper (str): The id of the query paper node in the graph.\n",
" comp_paper (str): The id of the comparison paper node in the graph.\n",
"\n",
" Returns:\n",
" dot_prod_res (Tensor): The dot product result representing the similarity between the query paper and the comparison paper.\n",
" \"\"\"\n",
"\n",
" # Convert the graph nodes into a list to work with indices\n",
" nodes_list = list(G.nodes()) # Assuming G_cite is a pre-defined graph variable\n",
"\n",
" # Find the indices of the query and comparison papers in the nodes list\n",
" query_and_comp_edge_ind = ([nodes_list.index(query_paper)], [nodes_list.index(comp_paper)])\n",
"\n",
" # Use the GNN's decode method to compute the dot product similarity between the embeddings of the two papers\n",
" dot_prod_res = gnn.decode(H, query_and_comp_edge_ind)\n",
"\n",
" return dot_prod_res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"RankMetricsCalculator class to calculate all metrics related stuff and improve the structure"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"class RankMetricsCalculator:\n",
" \"\"\"\n",
" A class to calculate various ranking metrics including accuracy, normalized discounted cumulative gain (NDCG),\n",
" mean reciprocal rank (MRR), and precision at rank 1 (Rprec/P@1), along with their adjusted versions.\n",
" \"\"\"\n",
"\n",
" def __init__(self):\n",
" # Counts of items ranked in correct and incorrect order\n",
" self.correct_order = 0\n",
" self.incorrect_order = 0\n",
"\n",
" # Accumulators for metrics\n",
" self.mrr = 0\n",
" self.ndcg_numerator = 0\n",
" self.rprec = 0\n",
"\n",
" # Adjusted metrics accumulators\n",
" self.adj_demonimator = 0\n",
" self.adj_ndcg_numerator = 0\n",
" self.adj_correct_order = 0\n",
" self.adj_incorrect_order = 0\n",
" self.adj_mrr = 0\n",
" self.adj_rprec = 0\n",
"\n",
" def calculate_metrics(self, neg_scores, pos_score, adj):\n",
" \"\"\"\n",
" Calculates and updates ranking metrics based on the scores of negative (incorrect) and positive (correct) instances,\n",
" and an adjustment factor for adjusted metrics.\n",
"\n",
" Parameters:\n",
" neg_scores (list of float): Scores of negative instances.\n",
" pos_score (float): Score of the positive instance.\n",
" adj (float): Adjustment factor for calculating adjusted metrics.\n",
" \"\"\"\n",
" # Count how many negative scores are ranked above and below the positive score\n",
" ranked_above = sum(1 for x in neg_scores if x > pos_score)\n",
" ranked_below = sum(1 for x in neg_scores if x < pos_score)\n",
"\n",
" # Handle ties by assigning half a point to both above and below for each tie\n",
" ranked_above += sum(0.5 for x in neg_scores if x == pos_score)\n",
" ranked_below += sum(0.5 for x in neg_scores if x == pos_score)\n",
"\n",
" # Update counts of correct and incorrect orders\n",
" self.correct_order += ranked_below\n",
" self.incorrect_order += ranked_above\n",
" \n",
" # Calculate and update metrics\n",
" ndcg = 1 / np.log2(2 + ranked_above) # NDCG calculation for the current instance\n",
" self.mrr += 1 / (1 + ranked_above) # MRR update\n",
" self.ndcg_numerator += ndcg # NDCG numerator update\n",
" self.rprec += 1 if ranked_above == 0 else 0 # Rprec/P@1 update\n",
" \n",
" # Calculate and update adjusted metrics using the adjustment factor\n",
" self.adj_demonimator += adj\n",
" self.adj_ndcg_numerator += adj * ndcg\n",
" self.adj_correct_order += adj * ranked_below\n",
" self.adj_incorrect_order += adj * ranked_above\n",
" self.adj_mrr += adj * (1 / (1 + ranked_above))\n",
" self.adj_rprec += adj * (1 if ranked_above == 0 else 0)\n",
"\n",
" def get_results(self, clicks):\n",
" \"\"\"\n",
" Calculates and returns the final metrics based on the accumulated values and the number of clicks (instances).\n",
"\n",
" Parameters:\n",
" clicks (list): The list of clicks or instances considered for the metrics calculation.\n",
"\n",
" Returns:\n",
" metrics (dict): A dictionary containing the calculated metrics.\n",
" \"\"\"\n",
" metrics = {\n",
" \"accuracy\": self.correct_order / (self.correct_order + self.incorrect_order),\n",
" \"ndcg\": self.ndcg_numerator / len(clicks),\n",
" \"mrr\": self.mrr / len(clicks),\n",
" \"Rprec/P@1\": self.rprec / len(clicks),\n",
" \"Adj-accuracy\": self.adj_correct_order / (self.adj_incorrect_order + self.adj_correct_order),\n",
" \"Adj-ndcg\": self.adj_ndcg_numerator / self.adj_demonimator,\n",
" \"Adj-mrr\": self.adj_mrr / self.adj_demonimator,\n",
" \"Adj-Rprec/P@1\": self.adj_rprec / self.adj_demonimator\n",
" }\n",
" return metrics\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 71/71 [00:00<00:00, 611.93it/s]\n"
]
},
{
"data": {
"text/plain": [
"'SPECTER 2:'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'accuracy': 0.6658291457286433,\n",
" 'ndcg': 0.6408412230074018,\n",
" 'mrr': 0.5243125419181757,\n",
" 'Rprec/P@1': 0.2676056338028169,\n",
" 'Adj-accuracy': tensor(0.6588),\n",
" 'Adj-ndcg': tensor(0.6113),\n",
" 'Adj-mrr': tensor(0.4861),\n",
" 'Adj-Rprec/P@1': tensor(0.2285)}"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'GNN'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'accuracy': 0.5985915492957746,\n",
" 'ndcg': 0.8510211816530542,\n",
" 'mrr': 0.7981220657276995,\n",
" 'Rprec/P@1': 0.5915492957746479,\n",
" 'Adj-accuracy': tensor(0.6052),\n",
" 'Adj-ndcg': tensor(0.8538),\n",
" 'Adj-mrr': tensor(0.8019),\n",
" 'Adj-Rprec/P@1': tensor(0.6010)}"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metrics_calculator_specter = RankMetricsCalculator()\n",
"metrics_calculator_gnn = RankMetricsCalculator()\n",
"\n",
"for line in tqdm(clicks):\n",
" # Prepare ids from file\n",
" [query_id, clicked_id, other_id_str] = list(csv.reader([line], delimiter=',', quotechar='\"'))[0]\n",
" other_ids = other_id_str.strip().split(\",\")\n",
" position = 1\n",
" paper_score_specter = [] #used for output\n",
" paper_score_gnn = [] #used for output\n",
"\n",
" # Find positive query-recom scores\n",
" clicked_position = other_ids.index(clicked_id) + 1\n",
" pos_score_specter = find_L2_distance_SPECTER2(sp2_embs, query_id, clicked_id)\n",
" pos_score_gnn = get_dot_prod_GNN(H, G_cite, query_id, clicked_id)\n",
" paper_score_specter.append([clicked_id, pos_score_specter])\n",
" paper_score_gnn.append([clicked_id, pos_score_gnn])\n",
"\n",
" # Find negative query-recom scores\n",
" neg_scores_specter = []\n",
" neg_scores_gnn = []\n",
" adj = 1 / adjClickDistribution[clicked_position-1]\n",
" for other_id in other_ids:\n",
" if not other_id == clicked_id:\n",
" neg_score_specter = find_L2_distance_SPECTER2(sp2_embs, query_id, other_id)\n",
" neg_score_gnn = get_dot_prod_GNN(H, G_cite, query_id, other_id)\n",
" neg_scores_specter.append(neg_score_specter)\n",
" neg_scores_gnn.append(neg_score_gnn)\n",
" paper_score_specter.append([other_id, neg_score_specter])\n",
" paper_score_gnn.append([other_id, neg_score_gnn])\n",
" position = position + 1\n",
" \n",
" metrics_calculator_specter.calculate_metrics(neg_scores_specter, pos_score_specter, adj)\n",
" metrics_calculator_gnn.calculate_metrics(neg_score_gnn, pos_score_gnn, adj)\n",
"\n",
"metrics_specter = metrics_calculator_specter.get_results(clicks)\n",
"metrics_gnn = metrics_calculator_gnn.get_results(clicks)\n",
"\n",
"display('SPECTER 2:', metrics_specter,\n",
" 'GNN', metrics_gnn)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# SPECTER-2\n",
"# {'accuracy': 0.6658291457286433,\n",
"# 'ndcg': 0.6408412230074018,\n",
"# 'mrr': 0.5243125419181757,\n",
"# 'Rprec/P@1': 0.2676056338028169,\n",
"# 'Adj-accuracy': tensor(0.6588),\n",
"# 'Adj-ndcg': tensor(0.6113),\n",
"# 'Adj-mrr': tensor(0.4861),\n",
"# 'Adj-Rprec/P@1': tensor(0.2285)}\n",
"\n",
"# SPECTER\n",
"# {'accuracy': 0.6256281407035176,\n",
"# 'ndcg': 0.6118552846993199,\n",
"# 'mrr': 0.4871450927788954,\n",
"# 'Rprec/P@1': 0.23943661971830985,\n",
"# 'Adj-accuracy': tensor(0.6344),\n",
"# 'Adj-ndcg': tensor(0.5911),\n",
"# 'Adj-mrr': tensor(0.4601),\n",
"# 'Adj-Rprec/P@1': tensor(0.2075)}\n",
"\n",
"# GNN (NEW result)\n",
"# {'accuracy': 0.5985915492957746,\n",
"# 'ndcg': 0.8510211816530542,\n",
"# 'mrr': 0.7981220657276995,\n",
"# 'Rprec/P@1': 0.5915492957746479,\n",
"# 'Adj-accuracy': tensor(0.6052),\n",
"# 'Adj-ndcg': tensor(0.8538),\n",
"# 'Adj-mrr': tensor(0.8019),\n",
"# 'Adj-Rprec/P@1': tensor(0.6010)}\n",
"\n",
"# GNN (OLD result)\n",
"# {'accuracy': 0.5841708542713567,\n",
"# 'ndcg': 0.6051892136642196,\n",
"# 'mrr': 0.4803330081499094,\n",
"# 'Rprec/P@1': 0.23943661971830985,\n",
"# 'Adj-accuracy': tensor(0.5528),\n",
"# 'Adj-ndcg': tensor(0.5593),\n",
"# 'Adj-mrr': tensor(0.4214),\n",
"# 'Adj-Rprec/P@1': tensor(0.1808)}"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
},
"vscode": {
"interpreter": {
"hash": "7d5edf4258bbc637805e302ba85a0073c54e6d301b29d811b35bf8e759b824fe"
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},
"nbformat": 4,
"nbformat_minor": 4
}
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