polish doc strings

pe/api/image/improved_diffusion_api.py

@@ -131,7 +131,7 @@ def random_api(self, label_info, num_samples):
         :param num_samples: The number of random samples to generate
         :type num_samples: int
         :return: The data object of the generated synthetic data
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         label_name = label_info.name
         execution_logger.info(f"RANDOM API: creating {num_samples} samples for label {label_name}")
@@ -164,9 +164,9 @@ def variation_api(self, syn_data):
         """Generating variations of the synthetic data.
 
         :param syn_data: The data object of the synthetic data
-        :type syn_data: :py:class:`pe.data.data.Data`
+        :type syn_data: :py:class:`pe.data.Data`
         :return: The data object of the variation of the input synthetic data
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         execution_logger.info(f"VARIATION API: creating variations for {len(syn_data.data_frame)} samples")
         images = np.stack(syn_data.data_frame[IMAGE_DATA_COLUMN_NAME].values)

pe/embedding/text/sentence_transformer.py

@@ -32,9 +32,9 @@ def compute_embedding(self, data):
         """Compute the Sentence Transformers embedding of text.
 
         :param data: The data object containing the text
-        :type data: :py:class:`pe.data.data.Data`
+        :type data: :py:class:`pe.data.Data`
         :return: The data object with the computed embedding
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         uncomputed_data = self.filter_uncomputed_rows(data)
         if len(uncomputed_data.data_frame) == 0:

pe/llm/huggingface/huggingface.py

@@ -12,7 +12,11 @@ class HuggingfaceLLM(LLM):
     def __init__(self, model_name_or_path, batch_size=128, dry_run=False, **generation_args):
         """Constructor.
 
-        :param model_name_or_path: The model name or path of the Huggingface model
+        :param model_name_or_path: The model name or path of the Huggingface model. Note that we use the FastChat
+            library (https://github.com/lm-sys/FastChat) to manage the conversation template. If the conversation
+            template of your desired model is not available in FastChat, please register the conversation template in
+            the FastChat library. See the following link for an example:
+            https://github.com/microsoft/DPSDA/blob/main/pe/llm/huggingface/register_fastchat/gpt2.py
         :type model_name_or_path: str
         :param batch_size: The batch size to use for generating the responses, defaults to 128
         :type batch_size: int, optional
@@ -91,7 +95,7 @@ def get_responses(self, requests, **generation_args):
         """Get the responses from the LLM.
 
         :param requests: The requests
-        :type requests: list[:py:class:`pe.llm.request.Request`]
+        :type requests: list[:py:class:`pe.llm.Request`]
         :param \\*\\*generation_args: The generation arguments. The priority of the generation arguments from the
             highest to the lowerest is in the order of: the arguments set in the requests > the arguments passed to
             this function > and the arguments passed to the constructor

pe/population/pe_population.py

@@ -24,7 +24,7 @@ def __init__(
         """Constructor.
 
         :param api: The API object that contains the random and variation APIs
-        :type api: :py:class:`pe.api.api.API`
+        :type api: :py:class:`pe.api.API`
         :param histogram_threshold: The threshold for clipping the histogram. None means no clipping. Defaults to None
         :type histogram_threshold: float, optional
         :param initial_variation_api_fold: The number of variations to apply to the initial synthetic data, defaults to
@@ -61,7 +61,7 @@ def initial(self, label_info, num_samples):
         :param num_samples: The number of samples to generate
         :type num_samples: int
         :return: The initial synthetic data
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         execution_logger.info(
             f"Population: generating {num_samples}*{self._initial_variation_api_fold + 1} initial "
@@ -83,10 +83,10 @@ def _post_process_histogram(self, syn_data):
         """Post process the histogram of synthetic data (e.g., clipping).
 
         :param syn_data: The synthetic data
-        :type syn_data: :py:class:`pe.data.data.Data`
+        :type syn_data: :py:class:`pe.data.Data`
         :return: The synthetic data with post-processed histogram in the column
             :py:const:`pe.constant.data.POST_PROCESSED_DP_HISTOGRAM_COLUMN_NAME`
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         count = syn_data.data_frame[DP_HISTOGRAM_COLUMN_NAME].to_numpy()
         if self._histogram_threshold is not None:
@@ -101,12 +101,12 @@ def _select_data(self, syn_data, num_samples):
         """Select data from the synthetic data according to `selection_mode`.
 
         :param syn_data: The synthetic data
-        :type syn_data: :py:class:`pe.data.data.Data`
+        :type syn_data: :py:class:`pe.data.Data`
         :param num_samples: The number of samples to select
         :type num_samples: int
         :raises ValueError: If the selection mode is not supported
         :return: The selected data
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         if self._selection_mode == "sample":
             count = syn_data.data_frame[POST_PROCESSED_DP_HISTOGRAM_COLUMN_NAME].to_numpy()
@@ -128,11 +128,11 @@ def next(self, syn_data, num_samples):
         """Generate the next synthetic data.
 
         :param syn_data: The synthetic data
-        :type syn_data: :py:class:`pe.data.data.Data`
+        :type syn_data: :py:class:`pe.data.Data`
         :param num_samples: The number of samples to generate
         :type num_samples: int
         :return: The next synthetic data
-        :rtype: :py:class:`pe.data.data.Data`
+        :rtype: :py:class:`pe.data.Data`
         """
         execution_logger.info(
             f"Population: generating {num_samples}*{self._next_variation_api_fold} " "next synthetic samples"