diff --git a/exploration/ex_ml_pipeline.py b/exploration/ex_ml_pipeline.py
index c91b8f7..d37b335 100644
--- a/exploration/ex_ml_pipeline.py
+++ b/exploration/ex_ml_pipeline.py
@@ -180,3 +180,17 @@ sensor_features.calculate_features()
 sensor_features.get_features("proximity", "all")
 
 # %%
+with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
+    labels_params = yaml.safe_load(file)
+
+# %%
+labels = pipeline.Labels(**labels_params)
+labels.questionnaires
+
+# %%
+labels.set_labels()
+
+# %%
+labels.get_labels("PANAS")
+
+# %%
diff --git a/machine_learning/config/minimal_labels.yaml b/machine_learning/config/minimal_labels.yaml
new file mode 100644
index 0000000..9e719ed
--- /dev/null
+++ b/machine_learning/config/minimal_labels.yaml
@@ -0,0 +1,6 @@
+grouping_variable: date_lj
+labels:
+  PANAS:
+    - PA
+    - NA
+participants_usernames: [nokia_0000003]
diff --git a/machine_learning/pipeline.py b/machine_learning/pipeline.py
index f08d2cf..d5ead2f 100644
--- a/machine_learning/pipeline.py
+++ b/machine_learning/pipeline.py
@@ -79,6 +79,51 @@ class SensorFeatures:
             raise KeyError("This data type has not been implemented.")
 
 
+class Labels:
+    def __init__(
+        self,
+        grouping_variable: str,
+        labels: dict,
+        participants_usernames: Collection = None,
+    ):
+        self.grouping_variable = grouping_variable
+
+        self.questionnaires = labels.keys()
+
+        if participants_usernames is None:
+            participants_usernames = participants.query_db.get_usernames(
+                collection_start=datetime.date.fromisoformat("2020-08-01")
+            )
+        self.participants_usernames = participants_usernames
+
+        self.df_esm = pd.DataFrame()
+        self.df_esm_preprocessed = pd.DataFrame()
+        self.df_esm_interest = pd.DataFrame()
+        self.df_esm_clean = pd.DataFrame()
+
+    def set_labels(self):
+        self.df_esm = esm.get_esm_data(self.participants_usernames)
+        self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
+        if "PANAS" in self.questionnaires:
+            self.df_esm_interest = self.df_esm_preprocessed[
+                (
+                    self.df_esm_preprocessed["questionnaire_id"]
+                    == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
+                )
+                | (
+                    self.df_esm_preprocessed["questionnaire_id"]
+                    == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
+                )
+            ]
+        self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
+
+    def get_labels(self, questionnaire):
+        if questionnaire == "PANAS":
+            return self.df_esm_clean
+        else:
+            raise KeyError("This questionnaire has not been implemented as a label.")
+
+
 class MachineLearningPipeline:
     def __init__(
         self,
@@ -117,21 +162,21 @@ class MachineLearningPipeline:
         self.df_esm_daily_means = pd.DataFrame()
         self.df_proximity_daily_counts = pd.DataFrame()
 
-    def get_labels(self):
-        self.df_esm = esm.get_esm_data(self.participants_usernames)
-        self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
-        if self.labels_questionnaire == "PANAS":
-            self.df_esm_interest = self.df_esm_preprocessed[
-                (
-                    self.df_esm_preprocessed["questionnaire_id"]
-                    == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
-                )
-                | (
-                    self.df_esm_preprocessed["questionnaire_id"]
-                    == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
-                )
-            ]
-        self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
+    # def get_labels(self):
+    #     self.df_esm = esm.get_esm_data(self.participants_usernames)
+    #     self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
+    #     if self.labels_questionnaire == "PANAS":
+    #         self.df_esm_interest = self.df_esm_preprocessed[
+    #             (
+    #                 self.df_esm_preprocessed["questionnaire_id"]
+    #                 == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
+    #             )
+    #             | (
+    #                 self.df_esm_preprocessed["questionnaire_id"]
+    #                 == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
+    #             )
+    #         ]
+    #     self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
 
     # def aggregate_daily(self):
     #     self.df_esm_daily_means = (