Add benchmark_multi_table_aws

sdgym/__init__.py

@@ -16,6 +16,7 @@
     benchmark_multi_table,
     benchmark_single_table,
     benchmark_single_table_aws,
+    benchmark_multi_table_aws,
 )
 from sdgym.cli.collect import collect_results
 from sdgym.cli.summary import make_summary_spreadsheet
@@ -36,6 +37,7 @@
     'DatasetExplorer',
     'ResultsExplorer',
     'benchmark_multi_table',
+    'benchmark_multi_table_aws',
     'benchmark_single_table',
     'benchmark_single_table_aws',
     'collect_results',

sdgym/benchmark.py

@@ -107,7 +107,7 @@
     'TVAESynthesizer',
 ]
 SDV_MULTI_TABLE_SYNTHESIZERS = ['HMASynthesizer']
-
+MODALITY_IDX = 10
 SDV_SYNTHESIZERS = SDV_SINGLE_TABLE_SYNTHESIZERS + SDV_MULTI_TABLE_SYNTHESIZERS
 
 
@@ -220,32 +220,46 @@ def _get_metainfo_increment(top_folder, s3_client=None):
     return max(increments) + 1 if increments else 0
 
 
-def _setup_output_destination_aws(output_destination, synthesizers, datasets, s3_client):
+def _setup_output_destination_aws(
+    output_destination,
+    synthesizers,
+    datasets,
+    modality,
+    s3_client,
+):
     paths = defaultdict(dict)
     s3_path = output_destination[len(S3_PREFIX) :].rstrip('/')
     parts = s3_path.split('/')
     bucket_name = parts[0]
     prefix_parts = parts[1:]
     paths['bucket_name'] = bucket_name
     today = datetime.today().strftime('%m_%d_%Y')
-    top_folder = '/'.join(prefix_parts + [f'SDGym_results_{today}'])
+
+    modality_prefix = '/'.join(prefix_parts + [modality])
+    top_folder = f'{modality_prefix}/SDGym_results_{today}'
     increment = _get_metainfo_increment(f's3://{bucket_name}/{top_folder}', s3_client)
     suffix = f'({increment})' if increment >= 1 else ''
     s3_client.put_object(Bucket=bucket_name, Key=top_folder + '/')
+    synthetic_data_extension = 'zip' if modality == 'multi_table' else 'csv'
     for dataset in datasets:
         dataset_folder = f'{top_folder}/{dataset}_{today}'
         s3_client.put_object(Bucket=bucket_name, Key=dataset_folder + '/')
-        paths[dataset]['meta'] = f's3://{bucket_name}/{dataset_folder}/meta.yaml'
+
         for synth_name in synthesizers:
             final_synth_name = f'{synth_name}{suffix}'
             synth_folder = f'{dataset_folder}/{final_synth_name}'
             s3_client.put_object(Bucket=bucket_name, Key=synth_folder + '/')
             paths[dataset][final_synth_name] = {
-                'synthesizer': f's3://{bucket_name}/{synth_folder}/{final_synth_name}.pkl',
-                'synthetic_data': f's3://{bucket_name}/{synth_folder}/{final_synth_name}_synthetic_data.csv',
-                'benchmark_result': f's3://{bucket_name}/{synth_folder}/{final_synth_name}_benchmark_result.csv',
-                'results': f's3://{bucket_name}/{top_folder}/results{suffix}.csv',
-                'metainfo': f's3://{bucket_name}/{top_folder}/metainfo{suffix}.yaml',
+                'synthesizer': (f's3://{bucket_name}/{synth_folder}/{final_synth_name}.pkl'),
+                'synthetic_data': (
+                    f's3://{bucket_name}/{synth_folder}/'
+                    f'{final_synth_name}_synthetic_data.{synthetic_data_extension}'
+                ),
+                'benchmark_result': (
+                    f's3://{bucket_name}/{synth_folder}/{final_synth_name}_benchmark_result.csv'
+                ),
+                'metainfo': (f's3://{bucket_name}/{top_folder}/metainfo{suffix}.yaml'),
+                'results': (f's3://{bucket_name}/{top_folder}/results{suffix}.csv'),
             }
 
     s3_client.put_object(
@@ -279,7 +293,9 @@ def _setup_output_destination(
             The s3 client that can be used to read / write to s3. Defaults to ``None``.
     """
     if s3_client:
-        return _setup_output_destination_aws(output_destination, synthesizers, datasets, s3_client)
+        return _setup_output_destination_aws(
+            output_destination, synthesizers, datasets, modality, s3_client
+        )
 
     if output_destination is None:
         return {}
@@ -1571,7 +1587,7 @@ def _get_s3_script_content(
     return f"""
 import boto3
 import cloudpickle
-from sdgym.benchmark import _run_jobs, _write_metainfo_file, _update_metainfo_file
+from sdgym.benchmark import _run_jobs, _write_metainfo_file, _update_metainfo_file, MODALITY_IDX
 from io import StringIO
 from sdgym.result_writer import S3ResultsWriter
 
@@ -1583,8 +1599,9 @@ def _get_s3_script_content(
 )
 response = s3_client.get_object(Bucket='{bucket_name}', Key='{job_args_key}')
 job_args_list = cloudpickle.loads(response['Body'].read())
+modality = job_args_list[0][MODALITY_IDX]
 result_writer = S3ResultsWriter(s3_client=s3_client)
-_write_metainfo_file({synthesizers}, job_args_list, 'single_table', result_writer)
+_write_metainfo_file({synthesizers}, job_args_list, modality, result_writer)
 scores = _run_jobs(None, job_args_list, False, result_writer=result_writer)
 metainfo_filename = job_args_list[0][-1]['metainfo']
 _update_metainfo_file(metainfo_filename, result_writer)
@@ -1619,7 +1636,7 @@ def _get_user_data_script(access_key, secret_key, region_name, script_content):
 
         echo "======== Install Dependencies in venv ============"
         pip install --upgrade pip
-        pip install sdgym[all]
+        pip install "sdgym[all] @ git+https://github.com/sdv-dev/SDGym.git@feature_branch/mutli_table_benchmark"
         pip install s3fs
 
         echo "======== Write Script ==========="
@@ -1644,13 +1661,14 @@ def _run_on_aws(
     aws_secret_access_key,
 ):
     bucket_name, job_args_key = _store_job_args_in_s3(output_destination, job_args_list, s3_client)
+    synthesizer_names = [{'name': synthesizer['name']} for synthesizer in synthesizers]
     script_content = _get_s3_script_content(
         aws_access_key_id,
         aws_secret_access_key,
         S3_REGION,
         bucket_name,
         job_args_key,
-        synthesizers,
+        synthesizer_names,
     )
 
     # Create a session and EC2 client using the provided S3 client's credentials
@@ -1917,3 +1935,102 @@ def benchmark_multi_table(
         _update_metainfo_file(metainfo_filename, result_writer)
 
     return scores
+
+
+def benchmark_multi_table_aws(
+    output_destination,
+    aws_access_key_id=None,
+    aws_secret_access_key=None,
+    synthesizers=DEFAULT_MULTI_TABLE_SYNTHESIZERS,
+    sdv_datasets=DEFAULT_MULTI_TABLE_DATASETS,
+    additional_datasets_folder=None,
+    limit_dataset_size=False,
+    compute_quality_score=True,
+    compute_diagnostic_score=True,
+    timeout=None,
+):
+    """Run the SDGym benchmark on multi-table datasets.
+
+    Args:
+        output_destination (str):
+            An S3 bucket or filepath. The results output folder will be written here.
+            Should be structured as:
+            s3://{s3_bucket_name}/{path_to_file} or s3://{s3_bucket_name}.
+        aws_access_key_id (str): The AWS access key id. Optional
+        aws_secret_access_key (str): The AWS secret access key. Optional
+        synthesizers (list[string]):
+            The synthesizer(s) to evaluate. Defaults to
+            ``[HMASynthesizer, MultiTableUniformSynthesizer]``. The available options
+            are:
+                - ``HMASynthesizer``
+                - ``MultiTableUniformSynthesizer``
+        sdv_datasets (list[str] or ``None``):
+            Names of the SDV demo datasets to use for the benchmark. Defaults to
+            ``[adult, alarm, census, child, expedia_hotel_logs, insurance, intrusion, news,
+            covtype]``. Use ``None`` to disable using any sdv datasets.
+        additional_datasets_folder (str or ``None``):
+            The path to an S3 bucket. Datasets found in this folder are
+            run in addition to the SDV datasets. If ``None``, no additional datasets are used.
+        limit_dataset_size (bool):
+            Use this flag to limit the size of the datasets for faster evaluation. If ``True``,
+            limit the size of every table to 1,000 rows (randomly sampled) and the first 10
+            columns.
+        compute_quality_score (bool):
+            Whether or not to evaluate an overall quality score. Defaults to ``True``.
+        compute_diagnostic_score (bool):
+            Whether or not to evaluate an overall diagnostic score. Defaults to ``True``.
+        timeout (int or ``None``):
+            The maximum number of seconds to wait for synthetic data creation. If ``None``, no
+            timeout is enforced.
+
+    Returns:
+        pandas.DataFrame:
+            A table containing one row per synthesizer + dataset.
+    """
+    s3_client = _validate_output_destination(
+        output_destination,
+        aws_keys={
+            'aws_access_key_id': aws_access_key_id,
+            'aws_secret_access_key': aws_secret_access_key,
+        },
+    )
+    if not synthesizers:
+        synthesizers = []
+
+    _ensure_uniform_included(synthesizers, modality='multi_table')
+    synthesizers = _import_and_validate_synthesizers(
+        synthesizers=synthesizers,
+        custom_synthesizers=None,
+        modality='multi_table',
+    )
+    job_args_list = _generate_job_args_list(
+        limit_dataset_size=limit_dataset_size,
+        sdv_datasets=sdv_datasets,
+        additional_datasets_folder=additional_datasets_folder,
+        sdmetrics=None,
+        timeout=timeout,
+        output_destination=output_destination,
+        compute_quality_score=compute_quality_score,
+        compute_diagnostic_score=compute_diagnostic_score,
+        compute_privacy_score=None,
+        synthesizers=synthesizers,
+        detailed_results_folder=None,
+        s3_client=s3_client,
+        modality='multi_table',
+    )
+    if not job_args_list:
+        return _get_empty_dataframe(
+            compute_diagnostic_score=compute_diagnostic_score,
+            compute_quality_score=compute_quality_score,
+            compute_privacy_score=None,
+            sdmetrics=None,
+        )
+
+    _run_on_aws(
+        output_destination=output_destination,
+        synthesizers=synthesizers,
+        s3_client=s3_client,
+        job_args_list=job_args_list,
+        aws_access_key_id=aws_access_key_id,
+        aws_secret_access_key=aws_secret_access_key,
+    )

sdgym/result_writer.py

@@ -154,3 +154,16 @@ def write_yaml(self, data, file_path, append=False):
         run_data.update(data)
         new_content = yaml.dump(run_data)
         self.s3_client.put_object(Body=new_content.encode(), Bucket=bucket, Key=key)
+
+    def write_zipped_dataframes(self, data, file_path, index=False):
+        """Write a dictionary of DataFrames to a ZIP file in S3."""
+        bucket, key = parse_s3_path(file_path)
+        zip_buffer = io.BytesIO()
+        with zipfile.ZipFile(zip_buffer, mode='w', compression=zipfile.ZIP_DEFLATED) as zf:
+            for table_name, table in data.items():
+                csv_buf = io.StringIO()
+                table.to_csv(csv_buf, index=index)
+                zf.writestr(f'{table_name}.csv', csv_buf.getvalue())
+
+        zip_buffer.seek(0)
+        self.s3_client.upload_fileobj(zip_buffer, bucket, key)