Add a function to load real-world datasets

causallearn/utils/Dataset.py

@@ -0,0 +1,41 @@
+import numpy as np
+import urllib.request
+from io import StringIO
+
+def load_dataset(dataset_name):
+    '''
+    Load real-world datasets. Processed datasets are from https://github.com/cmu-phil/example-causal-datasets/tree/main
+
+    Parameters
+    ----------
+    dataset_name : str, ['sachs', 'boston_housing', 'airfoil']
+
+    Returns
+    -------
+    data = np.array
+    labels = list
+    '''
+
+    url_mapping = {
+        'sachs': 'https://raw.githubusercontent.com/cmu-phil/example-causal-datasets/main/real/sachs/data/sachs.2005.continuous.txt',
+        'boston_housing': 'https://raw.githubusercontent.com/cmu-phil/example-causal-datasets/main/real/boston-housing/data/boston-housing.continuous.txt',
+        'airfoil': 'https://raw.githubusercontent.com/cmu-phil/example-causal-datasets/main/real/airfoil-self-noise/data/airfoil-self-noise.continuous.txt'
+    }
+
+    if dataset_name not in url_mapping:
+        raise ValueError("Invalid dataset name")
+
+    url = url_mapping[dataset_name]
+    with urllib.request.urlopen(url) as response:
+        content = response.read().decode('utf-8')  # Read content and decode to string
+
+    # Use StringIO to turn the string content into a file-like object so numpy can read from it
+    labels_array = np.genfromtxt(StringIO(content), delimiter="\t", dtype=str, max_rows=1)
+    data = np.loadtxt(StringIO(content), skiprows=1)
+
+    # Convert labels_array to a list of strings
+    labels_list = labels_array.tolist()
+    if isinstance(labels_list, str):  # handle the case where there's only one label
+        labels_list = [labels_list]
+
+    return data, labels_list