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Add demographic_features and targets module; refactor analysis code 

Co-authored-by: JulioV <juliovhz@gmail.com>
pull/95/head
Meng Li 2020-04-16 12:38:28 -04:00
parent 695984586f
commit eac721de84
12 changed files with 185 additions and 142 deletions
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33
Snakefile
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@ -11,12 +11,13 @@ rule all:
# My study (this is an example of a rule created specifically for a study)
expand("data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv",
pid=config["PIDS"],
days_before_surgery = config["METRICS_FOR_ANALYSIS"]["DAYS_BEFORE_SURGERY"],
days_after_discharge= config["METRICS_FOR_ANALYSIS"]["DAYS_AFTER_DISCHARGE"],
days_in_hospital= config["METRICS_FOR_ANALYSIS"]["DAYS_IN_HOSPITAL"]),
days_before_surgery = config["PARAMS_FOR_ANALYSIS"]["DAYS_BEFORE_SURGERY"],
days_after_discharge= config["PARAMS_FOR_ANALYSIS"]["DAYS_AFTER_DISCHARGE"],
days_in_hospital= config["PARAMS_FOR_ANALYSIS"]["DAYS_IN_HOSPITAL"]),
expand("data/processed/{pid}/targets_{summarised}.csv",
pid = config["PIDS"],
summarised = config["METRICS_FOR_ANALYSIS"]["SUMMARISED"]),
summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
expand("data/processed/{pid}/demographic_features.csv", pid=config["PIDS"]),
# Feature extraction
expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["FITBIT_TABLE"]),
@ -71,20 +72,20 @@ rule all:
pid=config["PIDS"],
segment = config["WIFI"]["DAY_SEGMENTS"]),
# Models
expand("data/processed/{pid}/metrics_for_individual_model/{source}_{day_segment}_original.csv",
expand("data/processed/{pid}/features_for_individual_model/{source}_{day_segment}_original.csv",
pid = config["PIDS"],
source = config["METRICS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["METRICS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
expand("data/processed/metrics_for_population_model/{source}_{day_segment}_original.csv",
source = config["METRICS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["METRICS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
expand("data/processed/{pid}/metrics_for_individual_model/{source}_{day_segment}_clean.csv",
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
expand("data/processed/features_for_population_model/{source}_{day_segment}_original.csv",
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
expand("data/processed/{pid}/features_for_individual_model/{source}_{day_segment}_clean.csv",
pid = config["PIDS"],
source = config["METRICS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["METRICS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
expand("data/processed/metrics_for_population_model/{source}_{day_segment}_clean.csv",
source = config["METRICS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["METRICS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
expand("data/processed/features_for_population_model/{source}_{day_segment}_clean.csv",
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
# Vizualisations
expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"]),

16
config.yaml
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@ -128,13 +128,14 @@ WIFI:
DAY_SEGMENTS: *day_segments
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
METRICS_FOR_ANALYSIS:
PARAMS_FOR_ANALYSIS:
GROUNDTRUTH_TABLE: participant_info
SOURCES: &sources ["phone_metrics", "fitbit_metrics", "phone_fitbit_metrics"]
SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]
DAY_SEGMENTS: *day_segments
PHONE_METRICS: [accelerometer, applications_foreground, battery, call_incoming, call_missed, call_outgoing, google_activity_recognition, light, location_barnett, screen, sms_received, sms_sent]
FITBIT_METRICS: [fitbit_heartrate, fitbit_step]
PHONE_FITBIT_METRICS: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
PHONE_FEATURES: [accelerometer, applications_foreground, battery, call_incoming, call_missed, call_outgoing, google_activity_recognition, light, location_barnett, screen, sms_received, sms_sent]
FITBIT_FEATURES: [fitbit_heartrate, fitbit_step]
PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays]
# Whether or not to include only days with enough valid sensed hours
# logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile
@ -154,3 +155,8 @@ METRICS_FOR_ANALYSIS:
COLS_VAR_THRESHOLD: True
ROWS_NAN_THRESHOLD: 0.5
PARTICIPANTS_DAY_THRESHOLD: 7
# Target Settings:
# 1 => TARGETS_RATIO_THRESHOLD (ceiling) or more of available CESD scores were TARGETS_VALUE_THRESHOLD or higher; 0 => otherwise
TARGETS_RATIO_THRESHOLD: 0.5
TARGETS_VALUE_THRESHOLD: 16

66
rules/models.snakefile
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@ -1,69 +1,69 @@
def input_merge_metrics_of_single_participant(wildcards):
if wildcards.source == "phone_fitbit_metrics":
return expand("data/processed/{pid}/{metrics}_{day_segment}.csv", pid=wildcards.pid, metrics=config["METRICS_FOR_ANALYSIS"]["PHONE_METRICS"] + config["METRICS_FOR_ANALYSIS"]["FITBIT_METRICS"], day_segment=wildcards.day_segment)
def input_merge_features_of_single_participant(wildcards):
if wildcards.source == "phone_fitbit_features":
return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"]["PHONE_FEATURES"] + config["PARAMS_FOR_ANALYSIS"]["FITBIT_FEATURES"], day_segment=wildcards.day_segment)
else:
return expand("data/processed/{pid}/{metrics}_{day_segment}.csv", pid=wildcards.pid, metrics=config["METRICS_FOR_ANALYSIS"][wildcards.source.upper()], day_segment=wildcards.day_segment)
return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"][wildcards.source.upper()], day_segment=wildcards.day_segment)
def optional_input_days_to_include(wildcards):
if config["METRICS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["ENABLED"]:
if config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["ENABLED"]:
# This input automatically trigers the rule days_to_analyse in mystudy.snakefile
return ["data/interim/{pid}/days_to_analyse" + \
"_" + str(config["METRICS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_BEFORE_SURGERY"]) + \
"_" + str(config["METRICS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_IN_HOSPITAL"]) + \
"_" + str(config["METRICS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_AFTER_DISCHARGE"]) + ".csv"]
"_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_BEFORE_SURGERY"]) + \
"_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_IN_HOSPITAL"]) + \
"_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_AFTER_DISCHARGE"]) + ".csv"]
else:
return []
def optional_input_valid_sensed_days(wildcards):
if config["METRICS_FOR_ANALYSIS"]["DROP_VALID_SENSED_DAYS"]["ENABLED"]:
if config["PARAMS_FOR_ANALYSIS"]["DROP_VALID_SENSED_DAYS"]["ENABLED"]:
# This input automatically trigers the rule phone_valid_sensed_days in preprocessing.snakefile
return ["data/interim/{pid}/phone_valid_sensed_days.csv"]
else:
return []
rule merge_metrics_for_individual_model:
rule merge_features_for_individual_model:
input:
metric_files = input_merge_metrics_of_single_participant,
feature_files = input_merge_features_of_single_participant,
phone_valid_sensed_days = optional_input_valid_sensed_days,
days_to_include = optional_input_days_to_include
params:
source = "{source}"
output:
"data/processed/{pid}/metrics_for_individual_model/{source}_{day_segment}_original.csv"
"data/processed/{pid}/features_for_individual_model/{source}_{day_segment}_original.csv"
script:
"../src/models/merge_metrics_for_individual_model.R"
"../src/models/merge_features_for_individual_model.R"
rule merge_metrics_for_population_model:
rule merge_targets_for_population_model:
input:
metric_files = expand("data/processed/{pid}/metrics_for_individual_model/{{source}}_{{day_segment}}_original.csv", pid=config["PIDS"])
data_files = expand("data/processed/{pid}/targets_{{summarised}}.csv", pid=config["PIDS"])
output:
"data/processed/metrics_for_population_model/{source}_{day_segment}_original.csv"
"data/processed/features_for_population_model/targets_{summarised}.csv"
script:
"../src/models/merge_metrics_for_population_model.R"
"../src/models/merge_data_for_population_model.py"
rule clean_metrics_for_individual_model:
rule clean_features_for_individual_model:
input:
rules.merge_metrics_for_individual_model.output
rules.merge_features_for_individual_model.output
params:
cols_nan_threshold = config["METRICS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
cols_var_threshold = config["METRICS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
rows_nan_threshold = config["METRICS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
participants_day_threshold = config["METRICS_FOR_ANALYSIS"]["PARTICIPANTS_DAY_THRESHOLD"]
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
participants_day_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANTS_DAY_THRESHOLD"]
output:
"data/processed/{pid}/metrics_for_individual_model/{source}_{day_segment}_clean.csv"
"data/processed/{pid}/features_for_individual_model/{source}_{day_segment}_clean.csv"
script:
"../src/models/clean_metrics_for_model.R"
"../src/models/clean_features_for_model.R"
rule clean_metrics_for_population_model:
rule clean_features_for_population_model:
input:
rules.merge_metrics_for_population_model.output
rules.merge_features_for_population_model.output
params:
cols_nan_threshold = config["METRICS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
cols_var_threshold = config["METRICS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
rows_nan_threshold = config["METRICS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
participants_day_threshold = config["METRICS_FOR_ANALYSIS"]["PARTICIPANTS_DAY_THRESHOLD"]
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
participants_day_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANTS_DAY_THRESHOLD"]
output:
"data/processed/metrics_for_population_model/{source}_{day_segment}_clean.csv"
"data/processed/features_for_population_model/{source}_{day_segment}_clean.csv"
script:
"../src/models/clean_metrics_for_model.R"
"../src/models/clean_features_for_model.R"

24
rules/mystudy.snakefile
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@ -1,6 +1,6 @@
rule days_to_analyse:
input:
participant_info = "data/raw/{pid}/" + config["METRICS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
params:
days_before_surgery = "{days_before_surgery}",
days_in_hospital = "{days_in_hospital}",
@ -10,12 +10,26 @@ rule days_to_analyse:
script:
"../src/models/select_days_to_analyse.py"
rule get_targets:
rule targets:
input:
participant_info = "data/raw/{pid}/" + config["METRICS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
params:
summarised = "{summarised}"
pid = "{pid}",
summarised = "{summarised}",
targets_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["TARGETS_RATIO_THRESHOLD"],
targets_value_threshold = config["PARAMS_FOR_ANALYSIS"]["TARGETS_VALUE_THRESHOLD"]
output:
"data/processed/{pid}/targets_{summarised}.csv"
script:
"../src/models/get_targets.py"
"../src/models/targets.py"
rule demographic_features:
input:
participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
params:
pid = "{pid}",
features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"]
output:
"data/processed/{pid}/demographic_features.csv"
script:
"../src/features/demographic_features.py"

17
src/features/demographic_features.py 100644
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@ -0,0 +1,17 @@
import pandas as pd
pid = snakemake.params["pid"]
requested_features = snakemake.params["features"]
demographic_features = pd.DataFrame(columns=["pid"] + requested_features)
participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"])
demographic_features.loc[0, "pid"] = pid
if not participant_info.empty:
if "age" in requested_features:
demographic_features.loc[0, "age"] = participant_info.loc[0, "age"]
if "gender" in requested_features:
demographic_features.loc[0, "gender"] = participant_info.loc[0, "gender"]
if "inpatientdays" in requested_features:
demographic_features.loc[0, "inpatientdays"] = (participant_info.loc[0, "discharge_date"] - participant_info.loc[0, "surgery_date"]).days
demographic_features.to_csv(snakemake.output[0], index=False)

40
src/models/clean_features_for_model.R 100644
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@ -0,0 +1,40 @@
source("packrat/init.R")
library(tidyr)
library(dplyr)
filter_participant_without_enough_days <- function(clean_features, participants_day_threshold){
if("pid" %in% colnames(clean_features))
clean_features <- clean_features %>% group_by(pid)
clean_features <- clean_features %>%
filter(n() >= participants_day_threshold) %>%
ungroup()
return(clean_features)
}
clean_features <- read.csv(snakemake@input[[1]])
cols_nan_threshold <- snakemake@params[["cols_nan_threshold"]]
drop_zero_variance_columns <- snakemake@params[["cols_var_threshold"]]
rows_nan_threshold <- snakemake@params[["rows_nan_threshold"]]
participants_day_threshold <- snakemake@params[["participants_day_threshold"]]
# We have to do this before and after dropping rows, that's why is duplicated
clean_features <- filter_participant_without_enough_days(clean_features, participants_day_threshold)
# drop columns with a percentage of NA values above cols_nan_threshold
if(nrow(clean_features))
clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold )
if(drop_zero_variance_columns)
clean_features <- clean_features %>% select_if(grepl("pid|local_date",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
# drop rows with a percentage of NA values above rows_nan_threshold
clean_features <- clean_features %>%
mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>%
filter(percentage_na < rows_nan_threshold) %>%
select(-percentage_na)
clean_features <- filter_participant_without_enough_days(clean_features, participants_day_threshold)
write.csv(clean_features, snakemake@output[[1]], row.names = FALSE)

40
src/models/clean_metrics_for_model.R
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@ -1,40 +0,0 @@
source("packrat/init.R")
library(tidyr)
library(dplyr)
filter_participant_without_enough_days <- function(clean_metrics, participants_day_threshold){
if("pid" %in% colnames(clean_metrics))
clean_metrics <- clean_metrics %>% group_by(pid)
clean_metrics <- clean_metrics %>%
filter(n() >= participants_day_threshold) %>%
ungroup()
return(clean_metrics)
}
clean_metrics <- read.csv(snakemake@input[[1]])
cols_nan_threshold <- snakemake@params[["cols_nan_threshold"]]
drop_zero_variance_columns <- snakemake@params[["cols_var_threshold"]]
rows_nan_threshold <- snakemake@params[["rows_nan_threshold"]]
participants_day_threshold <- snakemake@params[["participants_day_threshold"]]
# We have to do this before and after dropping rows, that's why is duplicated
clean_metrics <- filter_participant_without_enough_days(clean_metrics, participants_day_threshold)
# drop columns with a percentage of NA values above cols_nan_threshold
if(nrow(clean_metrics))
clean_metrics <- clean_metrics %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold )
if(drop_zero_variance_columns)
clean_metrics <- clean_metrics %>% select_if(grepl("pid|local_date",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
# drop rows with a percentage of NA values above rows_nan_threshold
clean_metrics <- clean_metrics %>%
mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>%
filter(percentage_na < rows_nan_threshold) %>%
select(-percentage_na)
clean_metrics <- filter_participant_without_enough_days(clean_metrics, participants_day_threshold)
write.csv(clean_metrics, snakemake@output[[1]], row.names = FALSE)

16
src/models/get_targets.py
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@ -1,16 +0,0 @@
import pandas as pd
participant_info = pd.read_csv(snakemake.input["participant_info"])
summarised = snakemake.params["summarised"]
pid = snakemake.input["participant_info"].split("/")[2]
targets = pd.DataFrame({"pid": [pid], "target": [None]})
if summarised == "summarised":
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
threshold_num = (cesds.count() + 1) // 2
threshold_cesd = 16
target = 1 if cesds.apply(lambda x : 1 if x >= threshold_cesd else 0).sum() >= threshold_num else 0
targets.loc[0, "target"] = target
targets.to_csv(snakemake.output[0], index=False)

24
src/models/merge_features_for_individual_model.R 100644
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@ -0,0 +1,24 @@
source("packrat/init.R")
library(tidyr)
library(purrr)
library(dplyr)
feature_files <- snakemake@input[["feature_files"]]
phone_valid_sensed_days <- snakemake@input[["phone_valid_sensed_days"]]
days_to_include <- snakemake@input[["days_to_include"]]
source <- snakemake@params[["source"]]
features_for_individual_model <- feature_files %>%
map(read.csv, stringsAsFactors = F, colClasses = c(local_date = "character")) %>%
reduce(full_join, by="local_date")
if(!is.null(phone_valid_sensed_days) && source %in% c("phone_features", "phone_fitbit_features")){
features_for_individual_model <- merge(features_for_individual_model, read.csv(phone_valid_sensed_days), by="local_date") %>% select(-valid_hours)
}
if(!is.null(days_to_include)){
features_for_individual_model <- merge(features_for_individual_model, read.csv(days_to_include), by="local_date")
}
write.csv(features_for_individual_model, snakemake@output[[1]], row.names = FALSE)

7
src/models/merge_metrics_for_population_model.R → src/models/merge_features_for_population_model.R
View File

@ -5,12 +5,13 @@ library(purrr)
library(dplyr)
library(stringr)
metric_files <- snakemake@input[["metric_files"]]
feature_files <- snakemake@input[["feature_files"]]
metrics_of_all_participants <- tibble(filename = metric_files) %>% # create a data frame
features_of_all_participants <- tibble(filename = feature_files) %>% # create a data frame
mutate(file_contents = map(filename, ~ read.csv(., stringsAsFactors = F, colClasses = c(local_date = "character"))),
pid = str_match(filename, ".*/([a-zA-Z]+?[0-9]+?)/.*")[,2]) %>%
unnest(cols = c(file_contents)) %>%
select(-filename)
write.csv(metrics_of_all_participants, snakemake@output[[1]], row.names = FALSE)
write.csv(features_of_all_participants, snakemake@output[[1]], row.names = FALSE)

24
src/models/merge_metrics_for_individual_model.R
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@ -1,24 +0,0 @@
source("packrat/init.R")
library(tidyr)
library(purrr)
library(dplyr)
metric_files <- snakemake@input[["metric_files"]]
phone_valid_sensed_days <- snakemake@input[["phone_valid_sensed_days"]]
days_to_include <- snakemake@input[["days_to_include"]]
source <- snakemake@params[["source"]]
metrics_for_individual_model <- metric_files %>%
map(read.csv, stringsAsFactors = F, colClasses = c(local_date = "character")) %>%
reduce(full_join, by="local_date")
if(!is.null(phone_valid_sensed_days) && source %in% c("phone_metrics", "phone_fitbit_metrics")){
metrics_for_individual_model <- merge(metrics_for_individual_model, read.csv(phone_valid_sensed_days), by="local_date") %>% select(-valid_hours)
}
if(!is.null(days_to_include)){
metrics_for_individual_model <- merge(metrics_for_individual_model, read.csv(days_to_include), by="local_date")
}
write.csv(metrics_for_individual_model, snakemake@output[[1]], row.names = FALSE)

20
src/models/targets.py 100644
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@ -0,0 +1,20 @@
import pandas as pd
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"]
if summarised == "summarised":
targets = pd.DataFrame(columns=["pid", "target"])
participant_info = pd.read_csv(snakemake.input["participant_info"])
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]
targets.to_csv(snakemake.output[0], index=False)