Add a rule to merge sensor and baseline features.

And select target as before.

config.yaml

@@ -645,7 +645,7 @@ PARAMS_FOR_ANALYSIS:
                 results-survey413767_final.csv  # Belgium 2
     ]
     QUESTION_LIST: survey637813+question_text.csv
-    FEATURES: [age, gender, startlanguage, demand, control, demand_control_ratio]
+    FEATURES: [age, gender, startlanguage, limesurvey_demand, limesurvey_control, limesurvey_demand_control_ratio, limesurvey_demand_control_ratio_quartile]
     CATEGORICAL_FEATURES: [gender]
 
   TARGET:

rules/models.smk

@@ -37,3 +37,14 @@ rule select_target:
         "data/processed/models/individual_model/{pid}/input.csv"
     script:
         "../src/models/select_targets.py"
+
+rule merge_features_and_targets_for_population_model:
+    input:
+        cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_rapids.csv",
+        demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
+    params:
+        target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
+    output:
+        "data/processed/models/population_model/input.csv"
+    script:
+        "../src/models/merge_features_and_targets_for_population_model.py"

src/models/merge_features_and_targets_for_population_model.py

@@ -0,0 +1,20 @@
+import pandas as pd
+
+from helper import retain_target_column
+
+sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"])
+
+all_baseline_features = pd.DataFrame()
+for baseline_features_path in snakemake.input["demographic_features"]:
+    pid = baseline_features_path.split("/")[3]
+    baseline_features = pd.read_csv(baseline_features_path)
+    baseline_features = baseline_features.assign(pid=pid)
+    all_baseline_features = pd.concat([all_baseline_features, baseline_features], axis=0)
+
+# merge sensor features and baseline features
+features = sensor_features.merge(all_baseline_features, on="pid", how="left")
+
+target_variable_name = snakemake.params["target_variable"]
+model_input = retain_target_column(features, target_variable_name)
+
+model_input.to_csv(snakemake.output[0], index=False)