Discard useless parameters and related code of example

rules/models.smk

@@ -17,9 +17,7 @@ rule targets:
         participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["TARGET_TABLE"] + "_raw.csv"
     params:
         pid = "{pid}",
-        summarised = "{summarised}",
-        targets_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["TARGETS_RATIO_THRESHOLD"],
-        targets_value_threshold = config["PARAMS_FOR_ANALYSIS"]["TARGETS_VALUE_THRESHOLD"]
+        summarised = "{summarised}"
     output:
         "data/processed/{pid}/targets_{summarised}.csv"
     script:

src/models/targets.py

@@ -3,19 +3,10 @@ import numpy as np
 
 pid = snakemake.params["pid"]
 summarised = snakemake.params["summarised"]
-targets_ratio_threshold = snakemake.params["targets_ratio_threshold"]
-targets_value_threshold = snakemake.params["targets_value_threshold"]
 participant_info = pd.read_csv(snakemake.input["participant_info"])
 
 if summarised == "summarised":
-    targets = pd.DataFrame(columns=["pid", "target"])
-
-    if not participant_info.empty:
-        cesds = participant_info.loc[0, ["preop_cesd_total", "inpatient_cesd_total", "postop_cesd_total", "3month_cesd_total"]]
-        # targets: 1 => 50% (ceiling) or more of available CESD scores were 16 or higher; 0 => otherwise
-        num_threshold = int((cesds.count() + 1) * targets_ratio_threshold)
-        target = 1 if cesds.apply(lambda x : 1 if x >= targets_value_threshold else 0).sum() >= num_threshold else 0
-        targets.loc[0, :] = [pid, target]
+    raise ValueError("Do not support summarised features for example dataset.")
 
 elif summarised == "notsummarised":
     targets = participant_info[["local_date", "target"]]