File size: 6,847 Bytes
afd9c6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | """
AgentEA-Studio: Data loading and statistics module.
"""
import json
import numpy as np
from src.config import DATASETS, ALIGNMENT_RESULTS, SAMPLE_ALIGNMENTS, ABLATION_RESULTS
def get_dataset_list():
"""Return list of available datasets."""
return list(DATASETS.keys())
def get_dataset_info(dataset_name):
"""Get dataset statistics as a formatted string."""
ds = DATASETS.get(dataset_name)
if not ds:
return "Dataset not found."
lines = [
f"## {ds['name']} ({ds['type']})",
"",
"| Property | {kg1} | {kg2} |".format(kg1=ds['kg1'], kg2=ds['kg2']),
"|---|---|---|",
f"| Entities | {ds['entities_kg1']:,} | {ds['entities_kg2']:,} |",
f"| Relations | {ds['relations_kg1']} | {ds['relations_kg2']} |",
f"| Facts | {ds['facts_kg1']:,} | {ds['facts_kg2']:,} |",
f"| Density | {ds['density_kg1']:.3f} | {ds['density_kg2']:.3f} |",
"",
"| Metric | Value |",
"|---|---|",
f"| Fact Count Difference | {ds['fact_diff_pct']}% |",
f"| Density Difference | {ds['density_diff_pct']}% |",
f"| Anchor Pairs | {ds['anchors']:,} |",
f"| Entity Overlap | {ds['overlapping']}% |",
f"| Structural Similarity | {ds['structural_similarity']}% |",
f"| Temporal | {'Yes' if ds['temporal'] else 'No'} |",
]
return "\n".join(lines)
def get_dataset_comparison():
"""Return a comparison table of all datasets."""
datasets = [
("ICEWS-WIKI", "HHKG"),
("ICEWS-YAGO", "HHKG"),
("DBP15K-EN-FR", "Cross-lingual"),
("DBP-WIKI", "Heterogeneous"),
]
header = "| Dataset | Type | Entities | Facts | Density | Overlap | Struct. Sim. |"
sep = "|---|---|---|---|---|---|---|"
rows = []
for name, dtype in datasets:
ds = DATASETS[name]
entities = f"{ds['entities_kg1']:,} / {ds['entities_kg2']:,}"
facts = f"{ds['facts_kg1']:,} / {ds['facts_kg2']:,}"
density = f"{ds['density_kg1']:.1f} / {ds['density_kg2']:.1f}"
overlap = f"{ds['overlapping']}%"
ssim = f"{ds['structural_similarity']}%"
rows.append(f"| {name} | {dtype} | {entities} | {facts} | {density} | {overlap} | {ssim} |")
return "\n".join([header, sep] + rows)
def get_heterogeneity_explanation(dataset_name):
"""Explain why a dataset is highly heterogeneous."""
ds = DATASETS.get(dataset_name)
if not ds:
return ""
points = []
if ds['fact_diff_pct'] > 100:
points.append(f"- **Fact count disparity**: {ds['kg1']} has {ds['fact_diff_pct']}% more facts than {ds['kg2']}")
if ds['density_diff_pct'] > 100:
points.append(f"- **Density disparity**: {ds['kg1']} density is {ds['density_diff_pct']}% higher than {ds['kg2']}")
if ds['overlapping'] < 100:
points.append(f"- **Low entity overlap**: Only {ds['overlapping']}% of entities appear in both KGs")
if ds['structural_similarity'] < 50:
points.append(f"- **Low structural similarity**: Only {ds['structural_similarity']}% — vastly different graph structures")
if not points:
return "This dataset has relatively homogeneous KGs."
return "\n".join(points)
def get_alignment_results(dataset_name):
"""Get pre-computed alignment results as a markdown table."""
results = ALIGNMENT_RESULTS.get(dataset_name)
if not results:
return "No results available."
header = "| Method | Hits@1 | Hits@5 | Hits@10 | MRR |"
sep = "|---|---|---|---|---|"
rows = []
for method, metrics in results.items():
h1 = f"{metrics['Hits@1']:.3f}"
h5 = f"{metrics['Hits@5']:.3f}"
h10 = f"{metrics['Hits@10']:.3f}"
mrr = f"{metrics['MRR']:.3f}"
rows.append(f"| {method} | {h1} | {h5} | {h10} | {mrr} |")
return "\n".join([header, sep] + rows)
def get_sample_alignments(dataset_name):
"""Get sample alignment pairs as a markdown table."""
samples = SAMPLE_ALIGNMENTS.get(dataset_name, [])
if not samples:
return "No samples available."
header = "| # | Entity in KG1 | Entity in KG2 | Type | Confidence |"
sep = "|---|---|---|---|---|"
rows = []
for i, s in enumerate(samples, 1):
rows.append(f"| {i} | {s['entity_kg1']} | {s['entity_kg2']} | {s['type']} | {s['confidence']:.1%} |")
return "\n".join([header, sep] + rows)
def get_ablation_results(dataset_name):
"""Get ablation study results as a markdown table."""
results = ABLATION_RESULTS.get(dataset_name)
if not results:
return "No ablation results available."
header = "| Configuration | Hits@1 | Hits@10 | MRR | Δ Hits@1 |"
sep = "|---|---|---|---|---|"
base_h1 = results["AdaCoAgentEA+"]["Hits@1"]
rows = []
for config, metrics in results.items():
h1 = f"{metrics['Hits@1']:.3f}"
h10 = f"{metrics['Hits@10']:.3f}"
mrr = f"{metrics['MRR']:.3f}"
delta = f"{metrics['Hits@1'] - base_h1:+.3f}"
rows.append(f"| {config} | {h1} | {h10} | {mrr} | {delta} |")
return "\n".join([header, sep] + rows)
def get_improvement_summary(dataset_name):
"""Get a summary of the improvement over best baseline."""
results = ALIGNMENT_RESULTS.get(dataset_name)
if not results:
return ""
ada_h1 = results["AdaCoAgentEA+"]["Hits@1"]
best_baseline_h1 = 0
best_baseline_name = ""
for method, metrics in results.items():
if method not in ["AdaCoAgentEA+", "AdaCoAgentEA"]:
if metrics["Hits@1"] > best_baseline_h1:
best_baseline_h1 = metrics["Hits@1"]
best_baseline_name = method
if best_baseline_h1 == 0:
return ""
relative_improvement = (ada_h1 - best_baseline_h1) / best_baseline_h1 * 100
return (
f"AdaCoAgentEA+ achieves **Hits@1 = {ada_h1:.1%}**, "
f"improving over the best baseline **{best_baseline_name}** "
f"(Hits@1 = {best_baseline_h1:.1%}) by **{relative_improvement:.1f}%** relative improvement."
)
def get_agent_details(agent_id):
"""Get details for a specific agent."""
from src.config import AGENTS
for area_key, area in AGENTS.items():
for agent in area["agents"]:
if agent["id"] == agent_id:
return f"""### {agent['icon']} {agent['name']} ({agent['id']})
**Type**: {agent['type']}
**Area**: {area['name']}
{agent['description']}
"""
return "Agent not found."
def get_pipeline_step(step_num):
"""Get details for a specific pipeline step."""
from src.config import PIPELINE_STEPS
for step in PIPELINE_STEPS:
if step["step"] == step_num:
return f"""### Step {step['step']}: {step['name']}
**Area**: {step['area']}
{step['description']}
**Details**: {step['details']}
"""
return "Step not found." |