[WIP] Methods to get the labels and data plus aggregate them.

exploration/ex_ml_pipeline.py

@@ -156,9 +156,20 @@ lin_reg_proximity.score(
 from machine_learning import pipeline
 
 # %%
-ml_pipeline = pipeline.MachineLearningPipeline()
+ml_pipeline = pipeline.MachineLearningPipeline(
+    labels_questionnaire="PANAS", data_types="proximity"
+)
+
+# %%
+ml_pipeline.get_labels()
+
+# %% tags=[]
+ml_pipeline.get_sensor_data()
+
+# %%
+ml_pipeline.aggregate_daily()
 
 # %%
-ml_pipeline.participants_usernames
+ml_pipeline.df_full_data_daily_means
 
 # %%

machine_learning/__init__.py

@@ -0,0 +1,7 @@
+QUESTIONNAIRE_IDS = {"PANAS": {"PA": 8.0, "NA": 9.0}}
+
+QUESTIONNAIRE_IDS_RENAME = {}
+
+for questionnaire in QUESTIONNAIRE_IDS.items():
+    for k, v in questionnaire[1].items():
+        QUESTIONNAIRE_IDS_RENAME[v] = k

machine_learning/pipeline.py

@@ -1,12 +1,79 @@
 import datetime
 
+import pandas as pd
+
 import participants.query_db
+from features import esm, helper, proximity
+from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
 
 
 class MachineLearningPipeline:
-    def __init__(self, participants_usernames=None):
+    def __init__(self, labels_questionnaire, data_types, participants_usernames=None):
         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.labels_questionnaire = labels_questionnaire
+        self.data_types = data_types
+
+        self.df_esm = pd.DataFrame()
+        self.df_esm_preprocessed = pd.DataFrame()
+        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()
+
+    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_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
+            )