Update functionality to download demos

sdv/errors.py

@@ -95,3 +95,20 @@ class RefitWarning(UserWarning):
 
 class SynthesizerProcessingError(Exception):
     """Error to raise when synthesizer parameters are invalid."""
+
+
+class DemoResourceNotFoundError(Exception):
+    """Raised when a demo dataset or one of its resources cannot be found.
+
+    This error is intended for missing demo assets such as the dataset archive,
+    metadata, license, README, or other auxiliary files in the demo bucket.
+    """
+
+
+class DemoResourceNotFoundWarning(UserWarning):
+    """Warning raised when an optional demo resource is not available.
+
+    This warning indicates that a non-critical artifact (e.g., README or SOURCE
+    information) is not present for a given demo dataset. The operation can
+    continue, but the requested information cannot be provided.
+    """

tests/integration/datasets/test_demo.py

@@ -1,220 +1,87 @@
 import pandas as pd
-from pandas.api.types import is_integer_dtype
 
 from sdv.datasets.demo import get_available_demos
 
 
 def test_get_available_demos_single_table():
-    """Test it can get demos for single table."""
+    """Test single_table demos listing equals the expected filtered list and values."""
     # Run
     tables_info = get_available_demos('single_table')
+    mask = ~(
+        tables_info['dataset_name'].str.startswith('bad_')
+        | tables_info['dataset_name'].str.startswith('dataset')
+    )
+    tables_info = tables_info[mask].reset_index(drop=True)
 
     # Assert
-    expected_table = pd.DataFrame({
+    expected = pd.DataFrame({
         'dataset_name': [
             'adult',
             'alarm',
             'census',
+            'census_extended',
             'child',
             'covtype',
             'expedia_hotel_logs',
+            'fake_companies',
+            'fake_hotel_guests',
             'insurance',
             'intrusion',
             'news',
+            'student_placements',
+            'student_placements_pii',
         ],
         'size_MB': [
-            '3.907448',
-            '4.520128',
-            '98.165608',
-            '3.200128',
-            '255.645408',
-            '0.200128',
-            '3.340128',
-            '162.039016',
-            '18.712096',
+            3.91,
+            4.52,
+            98.17,
+            4.95,
+            3.20,
+            255.65,
+            0.20,
+            0.00,
+            0.03,
+            3.34,
+            162.04,
+            18.71,
+            0.03,
+            0.03,
+        ],
+        'num_tables': [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
         ],
-        'num_tables': ['1'] * 9,
     })
-    expected_table['size_MB'] = expected_table['size_MB'].astype(float).round(2)
-    expected_table['num_tables'] = pd.to_numeric(expected_table['num_tables'])
-    assert is_integer_dtype(tables_info['num_tables'])
-    assert len(expected_table.merge(tables_info)) == len(expected_table)
+    pd.testing.assert_frame_equal(tables_info[['dataset_name', 'size_MB', 'num_tables']], expected)
 
 
 def test_get_available_demos_multi_table():
-    """Test it can get demos for multi table."""
+    """Test multi_table demos listing is returned with expected columns and types."""
     # Run
     tables_info = get_available_demos('multi_table')
 
     # Assert
-    expected_table = pd.DataFrame({
+    expected = pd.DataFrame({
         'dataset_name': [
-            'Accidents_v1',
-            'Atherosclerosis_v1',
-            'AustralianFootball_v1',
-            'Biodegradability_v1',
-            'Bupa_v1',
-            'CORA_v1',
-            'Carcinogenesis_v1',
-            'Chess_v1',
-            'Countries_v1',
-            'DCG_v1',
-            'Dunur_v1',
-            'Elti_v1',
-            'FNHK_v1',
-            'Facebook_v1',
-            'Hepatitis_std_v1',
-            'Mesh_v1',
-            'Mooney_Family_v1',
-            'MuskSmall_v1',
-            'NBA_v1',
-            'NCAA_v1',
-            'PTE_v1',
-            'Pima_v1',
-            'PremierLeague_v1',
-            'Pyrimidine_v1',
-            'SAP_v1',
-            'SAT_v1',
-            'SalesDB_v1',
-            'Same_gen_v1',
-            'Student_loan_v1',
-            'Telstra_v1',
-            'Toxicology_v1',
-            'Triazine_v1',
-            'TubePricing_v1',
-            'UTube_v1',
-            'UW_std_v1',
-            'WebKP_v1',
-            'airbnb-simplified',
-            'financial_v1',
-            'ftp_v1',
-            'genes_v1',
-            'got_families',
-            'imdb_MovieLens_v1',
-            'imdb_ijs_v1',
-            'imdb_small_v1',
-            'legalActs_v1',
-            'mutagenesis_v1',
-            'nations_v1',
-            'restbase_v1',
-            'rossmann',
-            'trains_v1',
-            'university_v1',
-            'walmart',
-            'world_v1',
+            'fake_hotels',
+            'fake_hotels_extended',
         ],
         'size_MB': [
-            '296.202744',
-            '7.916808',
-            '32.534832',
-            '0.692008',
-            '0.059144',
-            '1.987328',
-            '1.642592',
-            '0.403784',
-            '10.52272',
-            '0.321536',
-            '0.020224',
-            '0.054912',
-            '141.560872',
-            '1.481056',
-            '0.809472',
-            '0.101856',
-            '0.121784',
-            '0.646752',
-            '0.16632',
-            '29.137896',
-            '1.31464',
-            '0.160896',
-            '17.37664',
-            '0.038144',
-            '196.479272',
-            '0.500224',
-            '325.19768',
-            '0.056176',
-            '0.180256',
-            '5.503512',
-            '1.495496',
-            '0.156496',
-            '15.414536',
-            '0.135912',
-            '0.0576',
-            '1.9718',
-            '293.14392',
-            '94.718016',
-            '5.45568',
-            '0.440016',
-            '0.001',
-            '55.253264',
-            '259.140656',
-            '0.205728',
-            '186.132944',
-            '0.618088',
-            '0.540336',
-            '1.01452',
-            '73.328504',
-            '0.00644',
-            '0.009632',
-            '14.642184',
-            '0.295032',
-        ],
-        'num_tables': [
-            '3',
-            '4',
-            '4',
-            '5',
-            '9',
-            '3',
-            '6',
-            '2',
-            '4',
-            '2',
-            '17',
-            '11',
-            '3',
-            '2',
-            '7',
-            '29',
-            '68',
-            '2',
-            '4',
-            '9',
-            '38',
-            '9',
-            '4',
-            '2',
-            '4',
-            '36',
-            '4',
-            '4',
-            '10',
-            '5',
-            '4',
-            '2',
-            '20',
-            '2',
-            '4',
-            '3',
-            '2',
-            '8',
-            '2',
-            '3',
-            '3',
-            '7',
-            '7',
-            '7',
-            '5',
-            '3',
-            '3',
-            '3',
-            '2',
-            '2',
-            '5',
-            '3',
-            '3',
+            0.05,
+            0.07,
         ],
+        'num_tables': [2, 2],
     })
-    expected_table['size_MB'] = expected_table['size_MB'].astype(float).round(2)
-    expected_table['num_tables'] = pd.to_numeric(expected_table['num_tables'])
-    assert is_integer_dtype(tables_info['num_tables'])
-    assert len(expected_table.merge(tables_info, on='dataset_name')) == len(expected_table)
+    pd.testing.assert_frame_equal(tables_info[['dataset_name', 'size_MB', 'num_tables']], expected)