Return indices (#21)

* minor bugfix in adapters for sklearn

* adapter returns indicies of records

Author: jhoetter

refinery/__init__.py

@@ -81,6 +81,21 @@ def get_project_details(self) -> Dict[str, str]:
         api_response = api_calls.get_request(url, self.session_token)
         return api_response
 
+    def get_primary_keys(self) -> List[str]:
+        """Fetches the primary keys of your current project.
+
+        Returns:
+            List[str]: Containing the primary keys of your project.
+        """
+        project_details = self.get_project_details()
+        project_attributes = project_details["attributes"]
+
+        primary_keys = []
+        for attribute in project_attributes:
+            if attribute["is_primary_key"]:
+                primary_keys.append(attribute["name"])
+        return primary_keys
+
     def get_lookup_list(self, list_id: str) -> Dict[str, str]:
         """Fetches a lookup list of your current project.
 

refinery/adapter/sklearn.py

@@ -25,7 +25,7 @@ def build_classification_dataset(
         Dict[str, Dict[str, Any]]: Containing the train and test datasets, with embedded inputs.
     """
 
-    df_train, df_test, _ = split_train_test_on_weak_supervision(
+    df_train, df_test, _, primary_keys = split_train_test_on_weak_supervision(
         client, sentence_input, classification_label, num_train
     )
 
@@ -40,7 +40,12 @@ def build_classification_dataset(
     return {
         "train": {
             "inputs": inputs_train,
+            "index": df_train[primary_keys].to_dict("records"),
             "labels": df_train["label"],
         },
-        "test": {"inputs": inputs_test, "labels": df_test["label"]},
+        "test": {
+            "inputs": inputs_test,
+            "index": df_test[primary_keys].to_dict("records"),
+            "labels": df_test["label"],
+        },
     }

refinery/adapter/transformers.py

@@ -18,7 +18,12 @@ def build_classification_dataset(
         _type_: HuggingFace dataset
     """
 
-    df_train, df_test, label_options = split_train_test_on_weak_supervision(
+    (
+        df_train,
+        df_test,
+        label_options,
+        primary_keys,
+    ) = split_train_test_on_weak_supervision(
         client, sentence_input, classification_label
     )
 
@@ -44,4 +49,6 @@ def build_classification_dataset(
     if os.path.exists(test_file_path):
         os.remove(test_file_path)
 
-    return dataset, mapping
+    index = {"train": df_train[primary_keys], "test": df_test[primary_keys]}
+
+    return dataset, mapping, index

refinery/adapter/util.py

@@ -20,20 +20,27 @@ def split_train_test_on_weak_supervision(
         Tuple[pd.DataFrame, pd.DataFrame, List[str]]: Containing the train and test dataframes and the label name options.
     """
 
+    primary_keys = client.get_primary_keys()
+
     label_attribute_train = f"{_label}__WEAK_SUPERVISION"
     label_attribute_test = f"{_label}__MANUAL"
 
     df_test = client.get_record_export(
         tokenize=False,
-        keep_attributes=[_input, label_attribute_test],
+        keep_attributes=primary_keys + [_input, label_attribute_test],
         dropna=True,
     ).rename(columns={label_attribute_test: "label"})
 
+    if num_train is not None:
+        num_samples = num_train + len(df_test)
+    else:
+        num_samples = None
+
     df_train = client.get_record_export(
         tokenize=False,
-        keep_attributes=[_input, label_attribute_train],
+        keep_attributes=primary_keys + [_input, label_attribute_train],
         dropna=True,
-        num_samples=num_train + len(df_test),
+        num_samples=num_samples,
     ).rename(columns={label_attribute_train: "label"})
 
     # Remove overlapping data
@@ -47,4 +54,5 @@ def split_train_test_on_weak_supervision(
         df_train.reset_index(drop=True),
         df_test.reset_index(drop=True),
         label_options,
+        primary_keys,
     )