diff --git a/machine_learning/pipeline.py b/machine_learning/pipeline.py
index 5b57f53..572c7a5 100644
--- a/machine_learning/pipeline.py
+++ b/machine_learning/pipeline.py
@@ -8,6 +8,53 @@ from features import esm, helper, proximity
 from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
 
 
+class SensorFeatures:
+    def __init__(
+        self,
+        grouping_variable,
+        data_types,
+        feature_names=None,
+        participants_usernames=None,
+    ):
+        self.data_types = data_types
+        self.grouping_variable = grouping_variable
+
+        if feature_names is None:
+            self.feature_names = []
+
+        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_proximity = pd.DataFrame()
+        self.df_proximity_counts = pd.DataFrame()
+
+    def set_sensor_data(self):
+        if "proximity" in self.data_types:
+            self.df_proximity = proximity.get_proximity_data(
+                self.participants_usernames
+            )
+            self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
+            self.df_proximity = proximity.recode_proximity(self.df_proximity)
+
+    def get_sensor_data(self, data_type) -> pd.DataFrame:
+        # TODO implement the getter (Check if it has been set.)
+        return self.df_proximity
+
+    def calculate_features(self):
+        if "proximity" in self.data_types:
+            self.df_proximity_counts = proximity.count_proximity(
+                self.df_proximity, ["participant_id", self.grouping_variable]
+            )
+            # TODO Think about joining dataframes.
+
+    def get_features(self, data_type) -> pd.DataFrame:
+        # TODO implement the getter (Check if it has been set.)
+        return self.df_proximity_counts
+
+
 class MachineLearningPipeline:
     def __init__(
         self,
@@ -42,8 +89,6 @@ class MachineLearningPipeline:
         self.df_esm_interest = pd.DataFrame()
         self.df_esm_clean = pd.DataFrame()
 
-        self.df_proximity = pd.DataFrame()
-
         self.df_full_data_daily_means = pd.DataFrame()
         self.df_esm_daily_means = pd.DataFrame()
         self.df_proximity_daily_counts = pd.DataFrame()
@@ -64,39 +109,31 @@ class MachineLearningPipeline:
             ]
         self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
 
-    def get_sensor_data(self):
-        if "proximity" in self.data_types:
-            self.df_proximity = proximity.get_proximity_data(
-                self.participants_usernames
-            )
-            self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
-            self.df_proximity = proximity.recode_proximity(self.df_proximity)
-
-    def aggregate_daily(self):
-        self.df_esm_daily_means = (
-            self.df_esm_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
-            .esm_user_answer_numeric.agg("mean")
-            .reset_index()
-            .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
-        )
-        self.df_esm_daily_means = (
-            self.df_esm_daily_means.pivot(
-                index=["participant_id", "date_lj"],
-                columns="questionnaire_id",
-                values="esm_numeric_mean",
-            )
-            .reset_index(col_level=1)
-            .rename(columns=QUESTIONNAIRE_IDS_RENAME)
-            .set_index(["participant_id", "date_lj"])
-        )
-        self.df_full_data_daily_means = self.df_esm_daily_means.copy()
-        if "proximity" in self.data_types:
-            self.df_proximity_daily_counts = proximity.count_proximity(
-                self.df_proximity, ["participant_id", "date_lj"]
-            )
-            self.df_full_data_daily_means = self.df_full_data_daily_means.join(
-                self.df_proximity_daily_counts
-            )
+    # def aggregate_daily(self):
+    #     self.df_esm_daily_means = (
+    #         self.df_esm_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
+    #         .esm_user_answer_numeric.agg("mean")
+    #         .reset_index()
+    #         .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
+    #     )
+    #     self.df_esm_daily_means = (
+    #         self.df_esm_daily_means.pivot(
+    #             index=["participant_id", "date_lj"],
+    #             columns="questionnaire_id",
+    #             values="esm_numeric_mean",
+    #         )
+    #         .reset_index(col_level=1)
+    #         .rename(columns=QUESTIONNAIRE_IDS_RENAME)
+    #         .set_index(["participant_id", "date_lj"])
+    #     )
+    #     self.df_full_data_daily_means = self.df_esm_daily_means.copy()
+    #     if "proximity" in self.data_types:
+    #         self.df_proximity_daily_counts = proximity.count_proximity(
+    #             self.df_proximity, ["participant_id", "date_lj"]
+    #         )
+    #         self.df_full_data_daily_means = self.df_full_data_daily_means.join(
+    #             self.df_proximity_daily_counts
+    #         )
 
     def assign_columns(self):
         self.df_features = self.df_full_data_daily_means[self.feature_names]