CSE472-blanket-challenge/phase1-dataset

Phase 1 Dataset

Data Card

Field	Type	Description
data_id	string	Unique identifier (hash of graph_id + target + scm_type)
graph_id	string	Source graph identifier
X	List[List[float]]	Feature matrix (n_samples × n_features)
y	List[float]	Target variable values (n_samples,)
adjacency_matrix	List[List[int]]	NxN binary matrix where N = (n_feature + 1), A[i,j]=1 means i→j (target as last node)
feature_mask	List[int]	Binary mask for Markov blanket features (n_features,)
fs_method	string	method used to generate feature_mask
num_nodes	int	Number of variables in original graph
num_edges	int	Number of edges in DAG
density	float	Actual density (edges / max_possible_edges)
graph_generation_method	string	Graph generation method (e.g., "PA", "ER")

Dataset Generation Settings

• Graphs: See Graph Dataset
• Target Selection: Random node selection
• Data Generation: Linear or Nonlinear Gaussian SCM
  • Linear: $X_i = \sum_j(\beta_{ij} \cdot X_j) + \epsilon_i$
  • Nonlinear: $X_i = f(\sum_j(\beta_{ij} \cdot X_j)) + \epsilon_i$
• Coefficients $\beta_{ij}$ sampled from [-2, 2]
• Nonlinear functions: $x^2$, $\sin(x)$, $\cos(x)$, $\tanh(x)$, $x|x|$, $e^{-x^2}$
• Gaussian noise: $\epsilon_i \sim N(0, 0.5^2)$

Quick Start

Load Dataset

from datasets import load_dataset
import numpy as np

# Load dataset
linear_dataset = load_dataset("CSE472-blanket-challenge/phase1-dataset", split="train") # linear by default
nonlinear_dataset = load_dataset("CSE472-blanket-challenge/phase1-dataset", name="nonlinear", split="train")

record = linear_dataset[0]

print(f"X shape: {len(record['X'])} x {len(record['X'][0])}")  # n_samples x n_features
print(f"y shape: {len(record['y'])}")  # n_samples
print(f"Nodes: {record['num_nodes']}")
print(f"Graph density: {record['density']}")

Convert Adjacency Matrix to Graph

import networkx as nx
import numpy as np

# Get reordered adjacency matrix (target as last node)
adj_matrix = np.asarray(record["adjacency_matrix"])

# Create directed graph
dag = nx.from_numpy_array(adj_matrix, create_using=nx.DiGraph)

print(f"Nodes: {dag.number_of_nodes()}")
print(f"Edges: {dag.number_of_edges()}")
print(f"Is DAG: {nx.is_directed_acyclic_graph(dag)}")

Extract Features and Markov Blanket

# Get feature matrix and target
X = np.asarray(record["X"])  # (n_samples, n_features)
y = np.asarray(record["y"])  # (n_samples,)

# Get Markov blanket indices for features
mb_mask = np.asarray(record["markov_blanket"])
mb_indices = np.where(mb_mask == 1)[0]

print(f"Feature matrix shape: {X.shape}")
print(f"Target vector shape: {y.shape}")
print(f"Markov blanket features: {mb_indices}")
print(f"Total features in MB: {mb_mask.sum()}/{len(mb_mask)}")