Add communication features to pipeline.

exploration/ex_ml_pipeline.py

@@ -18,9 +18,9 @@
 import datetime
 import os
 import sys
-import yaml
 
 import seaborn as sns
+import yaml
 from sklearn import linear_model
 from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
 
@@ -157,9 +157,12 @@ lin_reg_proximity.score(
 from machine_learning import pipeline
 
 # %%
-with open('../machine_learning/config/minimal_features.yaml', 'r') as file:
+with open("../machine_learning/config/minimal_features.yaml", "r") as file:
     sensor_features = yaml.full_load(file)
 
+# %%
+sensor_features.get_sensor_data("proximity")
+
 # %%
 sensor_features.set_sensor_data()
 

machine_learning/pipeline.py

@@ -5,12 +5,12 @@ import yaml
 from sklearn.model_selection import cross_val_score
 
 import participants.query_db
-from features import esm, helper, proximity
+from features import communication, esm, helper, proximity
 from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
 
 
 class SensorFeatures(yaml.YAMLObject):
-    yaml_tag = u'!SensorFeatures'
+    yaml_tag = u"!SensorFeatures"
 
     def __init__(
         self,
@@ -34,6 +34,10 @@ class SensorFeatures(yaml.YAMLObject):
         self.df_proximity = pd.DataFrame()
         self.df_proximity_counts = pd.DataFrame()
 
+        self.df_calls = pd.DataFrame()
+        self.df_sms = pd.DataFrame()
+        self.df_calls_sms = pd.DataFrame()
+
     def set_sensor_data(self):
         if "proximity" in self.data_types:
             self.df_proximity = proximity.get_proximity_data(
@@ -41,6 +45,12 @@ class SensorFeatures(yaml.YAMLObject):
             )
             self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
             self.df_proximity = proximity.recode_proximity(self.df_proximity)
+        if "communication" in self.data_types:
+            self.df_calls = communication.get_call_data(self.participants_usernames)
+            self.df_calls = helper.get_date_from_timestamp(self.df_calls)
+
+            self.df_sms = communication.get_sms_data(self.participants_usernames)
+            self.df_sms = helper.get_date_from_timestamp(self.df_sms)
 
     def get_sensor_data(self, data_type) -> pd.DataFrame:
         if data_type == "proximity":
@@ -53,6 +63,10 @@ class SensorFeatures(yaml.YAMLObject):
             self.df_proximity_counts = proximity.count_proximity(
                 self.df_proximity, ["participant_id", self.grouping_variable]
             )
+        if "communication" in self.data_types:
+            self.df_calls_sms = communication.calls_sms_features(
+                df_calls=self.df_calls, df_sms=self.df_sms
+            )
             # TODO Think about joining dataframes.
 
     def get_features(self, data_type, feature_names) -> pd.DataFrame: