Prepare the first full pipeline.

machine_learning/config/prox_comm_PANAS_features.yaml

@@ -0,0 +1,6 @@
+grouping_variable: [date_lj]
+features:
+  proximity:
+    all
+  communication:
+    all

machine_learning/config/prox_comm_PANAS_labels.yaml

@@ -0,0 +1,5 @@
+grouping_variable: [date_lj]
+labels:
+  PANAS:
+    - PA
+    - NA

machine_learning/pipeline.py

@@ -1,7 +1,10 @@
 import datetime
 from collections.abc import Collection
 
+import numpy as np
 import pandas as pd
+import yaml
+from sklearn import linear_model
 from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
 
 import participants.query_db
@@ -343,3 +346,29 @@ class MachineLearningPipeline:
             cv=self.validation_method,
             n_jobs=-1,
         )
+
+
+if __name__ == "__main__":
+    with open("./config/prox_comm_PANAS_features.yaml", "r") as file:
+        sensor_features_params = yaml.safe_load(file)
+    sensor_features = SensorFeatures(**sensor_features_params)
+    sensor_features.set_sensor_data()
+    sensor_features.calculate_features()
+
+    with open("./config/prox_comm_PANAS_labels.yaml", "r") as file:
+        labels_params = yaml.safe_load(file)
+    labels = Labels(**labels_params)
+    labels.set_labels()
+    labels.aggregate_labels()
+
+    model_validation = ModelValidation(
+        sensor_features.get_features("all", "all"),
+        labels.get_aggregated_labels(),
+        group_variable="participant_id",
+        cv_name="loso",
+    )
+    model_validation.model = linear_model.LinearRegression()
+    model_validation.set_cv_method()
+    model_loso_r2 = model_validation.cross_validate()
+    print(model_loso_r2)
+    print(np.mean(model_loso_r2))