Add an option to read cached labels from a file.

exploration/ex_ml_pipeline.py

@@ -228,10 +228,20 @@ labels.set_labels()
 labels.get_labels("PANAS")
 
 # %%
-labels.aggregate_labels()
+labels.aggregate_labels(cached=False)
+labels_calculated = labels.get_aggregated_labels()
 
 # %%
-labels.get_aggregated_labels()
+labels.aggregate_labels(cached=True)
+labels_read = labels.get_aggregated_labels()
+labels_read = labels_read.reset_index()
+labels_read["date_lj"] = labels_read["date_lj"].dt.date
+labels_read.set_index(["participant_id", "date_lj"], inplace=True)
+# date_lj column is parsed as a date and represented as Timestamp, when read from csv.
+# When calculated, it is represented as date.
+
+# %%
+np.isclose(labels_read, labels_calculated).all()
 
 # %%
 model_validation = machine_learning.model.ModelValidation(

machine_learning/labels.py

@@ -9,7 +9,7 @@ from pyprojroot import here
 import participants.query_db
 from features import esm
 from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
-from machine_learning.helper import to_csv_with_settings
+from machine_learning.helper import to_csv_with_settings, read_csv_with_settings
 
 WARNING_PARTICIPANTS_LABEL = (
     "Before aggregating labels, please set participants label using self.set_participants_label() "
@@ -75,33 +75,48 @@ class Labels:
         else:
             raise KeyError("This questionnaire has not been implemented as a label.")
 
-    def aggregate_labels(self) -> None:
+    def aggregate_labels(self, cached=True) -> None:
         print("Aggregating labels ...")
-        self.df_esm_means = (
+        if not self.participants_label:
-            self.df_esm_clean.groupby(
+            raise ValueError(WARNING_PARTICIPANTS_LABEL)
-                ["participant_id", "questionnaire_id"] + self.grouping_variable
+
+        try:
+            if not cached:  # Do not use the file, even if it exists.
+                raise FileNotFoundError
+            self.df_esm_means = read_csv_with_settings(
+                self.folder,
+                self.filename_prefix,
+                data_type="_".join(self.questionnaires),
+                grouping_variable=self.grouping_variable
             )
-            .esm_user_answer_numeric.agg("mean")
+            print("Read labels from the file.")
-            .reset_index()
+        except FileNotFoundError:
-            .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
+            # We need to recalculate the features in this case.
-        )
+            self.df_esm_means = (
-        self.df_esm_means = (
+                self.df_esm_clean.groupby(
-            self.df_esm_means.pivot(
+                    ["participant_id", "questionnaire_id"] + self.grouping_variable
-                index=["participant_id"] + self.grouping_variable,
+                )
-                columns="questionnaire_id",
+                .esm_user_answer_numeric.agg("mean")
-                values="esm_numeric_mean",
+                .reset_index()
+                .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
+            )
+            self.df_esm_means = (
+                self.df_esm_means.pivot(
+                    index=["participant_id"] + self.grouping_variable,
+                    columns="questionnaire_id",
+                    values="esm_numeric_mean",
+                )
+                .reset_index(col_level=1)
+                .rename(columns=QUESTIONNAIRE_IDS_RENAME)
+                .set_index(["participant_id"] + self.grouping_variable)
+            )
+            print("Labels aggregated.")
+            to_csv_with_settings(
+                self.df_esm_means,
+                self.folder,
+                self.filename_prefix,
+                data_type="_".join(self.questionnaires),
             )
-            .reset_index(col_level=1)
-            .rename(columns=QUESTIONNAIRE_IDS_RENAME)
-            .set_index(["participant_id"] + self.grouping_variable)
-        )
-        print("Labels aggregated.")
-        to_csv_with_settings(
-            self.df_esm_means,
-            self.folder,
-            self.filename_prefix,
-            data_type="_".join(self.questionnaires),
-        )
 
     def get_aggregated_labels(self) -> pd.DataFrame:
         return self.df_esm_means