Fix Titanic dataset loading in test_utils.py

autokeras/test_utils.py

@@ -17,6 +17,7 @@
 
 import keras
 import numpy as np
+import pandas as pd
 
 import autokeras as ak
 
@@ -45,15 +46,53 @@
     "embark_town": "categorical",
     "alone": "categorical",
 }
-TRAIN_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
-TEST_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"
-
-TRAIN_CSV_PATH = keras.utils.get_file(
-    fname=os.path.basename(TRAIN_DATA_URL), origin=TRAIN_DATA_URL
-)
-TEST_CSV_PATH = keras.utils.get_file(
-    fname=os.path.basename(TEST_DATA_URL), origin=TEST_DATA_URL
-)
+
+# Download Titanic dataset from OpenML and split into train/test
+TITANIC_DATA_URL = "https://www.openml.org/data/get_csv/16826755/phpMYEkMl"
+
+# Download the dataset handling SSL issues
+import urllib.request
+import ssl
+_cache_dir = os.path.expanduser(os.path.join("~", ".keras", "datasets"))
+os.makedirs(_cache_dir, exist_ok=True)
+_titanic_data_path = os.path.join(_cache_dir, "titanic.csv")
+
+if not os.path.exists(_titanic_data_path):
+    # Create unverified SSL context to handle certificate issues
+    ssl_context = ssl._create_unverified_context()
+    with urllib.request.urlopen(TITANIC_DATA_URL, context=ssl_context) as response:
+        with open(_titanic_data_path, 'wb') as out_file:
+            out_file.write(response.read())
+
+# Load and preprocess the dataset to match expected format
+_df = pd.read_csv(_titanic_data_path)
+
+# Rename columns to match expected format
+_df = _df.rename(columns={
+    'pclass': 'class',
+    'sibsp': 'n_siblings_spouses',
+    'cabin': 'deck',
+    'embarked': 'embark_town'
+})
+
+# Create 'alone' column
+_df['alone'] = (_df['n_siblings_spouses'] + _df['parch'] == 0).astype(str)
+
+# Select only the columns we need in the expected order
+_columns_to_keep = ['sex', 'age', 'n_siblings_spouses', 'parch', 'fare', 
+                    'class', 'deck', 'embark_town', 'alone', 'survived']
+_df = _df[_columns_to_keep]
+
+# Split into train and test
+_train_size = int(len(_df) * 0.8)
+_train_df = _df.iloc[:_train_size]
+_test_df = _df.iloc[_train_size:]
+
+# Save train and test splits
+TRAIN_CSV_PATH = os.path.join(_cache_dir, "titanic_train.csv")
+TEST_CSV_PATH = os.path.join(_cache_dir, "titanic_test.csv")
+_train_df.to_csv(TRAIN_CSV_PATH, index=False)
+_test_df.to_csv(TEST_CSV_PATH, index=False)
 
 
 def generate_data(num_instances=100, shape=(32, 32, 3)):