Merge branch 'main' into kartikch/genai-metric-fix

responsibleai_text/responsibleai_text/managers/explainer_manager.py

@@ -29,6 +29,26 @@
                                                  Tokens)
 from responsibleai_text.utils.question_answering import QAPredictor
 
+try:
+    from interpret_text.generative.lime_tools.explainers import \
+        LocalExplanationSentenceEmbedder
+    interpret_text_explainers_installed = True
+except ImportError:
+    interpret_text_explainers_installed = False
+
+try:
+    from interpret_text.generative.model_lib.openai_tooling import ChatOpenAI
+    interpret_text_openai_tooling_installed = True
+except ImportError:
+    interpret_text_openai_tooling_installed = False
+
+try:
+    from sentence_transformers import SentenceTransformer
+    sentence_transformers_installed = True
+except ImportError:
+    sentence_transformers_installed = False
+
+
 CONTEXT = QuestionAnsweringFields.CONTEXT
 QUESTIONS = QuestionAnsweringFields.QUESTIONS
 SEP = Tokens.SEP
@@ -42,6 +62,7 @@
 MODEL = Metadata.MODEL
 EXPLANATION = '_explanation'
 TASK_TYPE = '_task_type'
+PROMPT = 'prompt'
 
 
 class ExplainerManager(BaseManager):
@@ -74,10 +95,13 @@ def __init__(self, model: Any, evaluation_examples: pd.DataFrame,
         """
         self._model = model
         self._target_column = target_column
-        if not isinstance(target_column, list):
+        if not isinstance(target_column, (list, type(None))):
             target_column = [target_column]
-        self._evaluation_examples = \
-            evaluation_examples.drop(columns=target_column)
+        if target_column is None:
+            self._evaluation_examples = evaluation_examples
+        else:
+            self._evaluation_examples = \
+                evaluation_examples.drop(columns=target_column)
         self._is_run = False
         self._is_added = False
         self._features = list(self._evaluation_examples.columns)
@@ -131,6 +155,73 @@ def compute(self):
                 eval_examples.append(question + SEP + context)
             self._explanation = [explainer_start(eval_examples),
                                  explainer_end(eval_examples)]
+        elif self._task_type == ModelTask.GENERATIVE_TEXT:
+            if not interpret_text_explainers_installed:
+                error = (
+                    "The required module"
+                    "'interpret_text.generative.lime_tools.explainers' "
+                    "is not installed."
+                )
+                raise RuntimeError(error)
+            if not interpret_text_openai_tooling_installed:
+                error = (
+                    "The required module"
+                    "'interpret_text.generative.model_lib.openai_tooling' "
+                    "is not installed."
+                )
+                raise RuntimeError(error)
+            if not sentence_transformers_installed:
+                error = (
+                    "The required package"
+                    "'sentence_transformers' "
+                    "is not installed."
+                )
+                raise RuntimeError(error)
+
+            if CONTEXT in self._evaluation_examples.columns and \
+                    QUESTIONS in self._evaluation_examples.columns:
+                context = self._evaluation_examples[CONTEXT]
+                questions = self._evaluation_examples[QUESTIONS]
+                eval_examples = []
+                for context, question in zip(context, questions):
+                    eval_examples.append(question + SEP + context)
+            elif PROMPT in self._evaluation_examples.columns:
+                eval_examples = self._evaluation_examples[PROMPT].tolist()
+            else:
+                raise ValueError(
+                    "Neither 'context'/'questions' nor 'prompt' columns "
+                    "are present in the evaluation_examples DataFrame"
+                )
+            sentence_embedder = SentenceTransformer('all-MiniLM-L6-v2')
+            explainer = LocalExplanationSentenceEmbedder(
+                sentence_embedder=sentence_embedder,
+                perturbation_model="removal",
+                partition_fn="sentences",
+                progress_bar=None)
+            max_completion = 50  # Define max tokens for the completion
+
+            api_settings = {
+                "api_type": self._model.model.api_type,
+                "api_base": self._model.model.api_base,
+                "api_version": self._model.model.api_version,
+                "api_key": self._model.model.api_key
+            }
+            model_wrapped = ChatOpenAI(
+                engine=self._model.model.engine,
+                encoding="cl100k_base",
+                api_settings=api_settings)
+            completions = model_wrapped.sample(
+                eval_examples, max_new_tokens=max_completion)
+
+            explanation = []
+            for i, completion in enumerate(completions):
+                attribution, parts = explainer.attribution(model_wrapped,
+                                                           eval_examples[i],
+                                                           completion,
+                                                           )
+                explanation.append((attribution, parts))
+
+            self._explanation = explanation
         else:
             raise ValueError("Unknown task type: {}".format(self._task_type))
 

responsibleai_text/setup.py

@@ -24,6 +24,10 @@
         'bert_score',
         'nltk',
         'rouge_score'
+    ],
+    "generative_text": [
+        'interpret_text',
+        'sentence_transformers'
     ]
 }
 setuptools.setup(