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Merge branch 'master' into run_test_participant

labels
junos 2022-03-01 11:40:17 +01:00
parent 43e5ac7918 f13a91044d
commit 70e077f6ab
4 changed files with 165 additions and 7 deletions
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3
Snakefile
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@ -403,9 +403,10 @@ for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +".csv"))
# Demographic features
# Baseline features
files_to_compute.extend(expand("data/raw/baseline_merged.csv"))
files_to_compute.extend(expand("data/raw/{pid}/participant_baseline_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]))
rule all:
input:

1
config.yaml
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@ -634,5 +634,6 @@ PARAMS_FOR_ANALYSIS:
results-survey358134_final.csv, # Belgium 1
results-survey413767_final.csv # Belgium 2
]
QUESTION_LIST: survey637813+question_text.csv
FEATURES: [age, gender, startlanguage]
CATEGORICAL_FEATURES: [gender]

6
rules/models.smk
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@ -20,8 +20,10 @@ rule baseline_features:
"data/raw/{pid}/participant_baseline_raw.csv"
params:
pid="{pid}",
features=config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FEATURES"]
features=config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FEATURES"],
question_filename=config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FOLDER"] + "/" + config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["QUESTION_LIST"]
output:
"data/processed/features/{pid}/baseline_features.csv"
interim="data/interim/{pid}/baseline_questionnaires.csv",
features="data/processed/features/{pid}/baseline_features.csv"
script:
"../src/data/baseline_features.py"

162
src/data/baseline_features.py
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@ -1,23 +1,177 @@
import numpy as np
import pandas as pd
pid = snakemake.params["pid"]
requested_features = snakemake.params["features"]
baseline_interim = pd.DataFrame(columns=["qid", "question", "score_original", "score"])
baseline_features = pd.DataFrame(columns=requested_features)
question_filename = snakemake.params["question_filename"]
JCQ_DEMAND = "JobEisen"
JCQ_CONTROL = "JobControle"
dict_JCQ_demand_control_reverse = {
JCQ_DEMAND: {
3: " [Od mene se ne zahteva,",
4: " [Imam dovolj časa, da končam",
5: " [Pri svojem delu se ne srečujem s konfliktnimi",
},
JCQ_CONTROL: {
2: " |Moje delo vključuje veliko ponavljajočega",
6: " [Pri svojem delu imam zelo malo svobode",
},
}
LIMESURVEY_JCQ_MIN = 1
LIMESURVEY_JCQ_MAX = 4
DEMAND_CONTROL_RATIO_MIN = 5 / (9 * 4)
DEMAND_CONTROL_RATIO_MAX = (4 * 5) / 9
JCQ_NORMS = {
"F": {
0: DEMAND_CONTROL_RATIO_MIN,
1: 0.45,
2: 0.52,
3: 0.62,
4: DEMAND_CONTROL_RATIO_MAX,
},
"M": {
0: DEMAND_CONTROL_RATIO_MIN,
1: 0.41,
2: 0.48,
3: 0.56,
4: DEMAND_CONTROL_RATIO_MAX,
},
}
participant_info = pd.read_csv(snakemake.input[0], parse_dates=["date_of_birth"])
if not participant_info.empty:
if "age" in requested_features:
now = pd.Timestamp("now")
baseline_features.loc[0, "age"] = (
now - participant_info.loc[0, "date_of_birth"]
).dt.days / 365.25245
).days / 365.25245
if "gender" in requested_features:
baseline_features.loc[0, "gender"] = participant_info.loc[0, "gender"]
if "startlanguage" in requested_features:
baseline_features.loc[0, "startlanguage"] = participant_info.loc[
0, "startlanguage"
]
if (
("demand" in requested_features)
or ("control" in requested_features)
or ("demand_control_ratio" in requested_features)
):
participant_info_t = participant_info.T
rows_baseline = participant_info_t.index
baseline_features.to_csv(
snakemake.output[0], index=False, encoding="utf-8",
)
if ("demand" in requested_features) or (
"demand_control_ratio" in requested_features
):
# Find questions about demand, but disregard time (duration of filling in questionnaire)
rows_demand = rows_baseline.str.startswith(
JCQ_DEMAND
) & ~rows_baseline.str.endswith("Time")
limesurvey_demand = (
participant_info_t[rows_demand]
.reset_index()
.rename(columns={"index": "question", 0: "score_original"})
)
# Extract question IDs from names such as JobEisen[3]
limesurvey_demand.loc[:, "qid"] = (
limesurvey_demand["question"].str.extract(r"\[(\d+)\]").astype(int)
)
limesurvey_demand["score"] = limesurvey_demand["score_original"]
# Identify rows that include questions to be reversed.
rows_demand_reverse = limesurvey_demand["qid"].isin(
dict_JCQ_demand_control_reverse[JCQ_DEMAND].keys()
)
# Reverse the score, so that the maximum value becomes the minimum etc.
limesurvey_demand.loc[rows_demand_reverse, "score"] = (
LIMESURVEY_JCQ_MAX
+ LIMESURVEY_JCQ_MIN
- limesurvey_demand.loc[rows_demand_reverse, "score_original"]
)
pd.concat([baseline_interim, limesurvey_demand], axis=0, ignore_index=True)
if "demand" in requested_features:
baseline_features.loc[0, "limesurvey_demand"] = limesurvey_demand[
"score"
].sum()
if ("control" in requested_features) or (
"demand_control_ratio" in requested_features
):
# Find questions about control, but disregard time (duration of filling in questionnaire)
rows_control = rows_baseline.str.startswith(
JCQ_CONTROL
) & ~rows_baseline.str.endswith("Time")
limesurvey_control = (
participant_info_t[rows_control]
.reset_index()
.rename(columns={"index": "question", 0: "score_original"})
)
# Extract question IDs from names such as JobControle[3]
limesurvey_control.loc[:, "qid"] = (
limesurvey_control["question"].str.extract(r"\[(\d+)\]").astype(int)
)
limesurvey_control["score"] = limesurvey_control["score_original"]
# Identify rows that include questions to be reversed.
rows_control_reverse = limesurvey_control["qid"].isin(
dict_JCQ_demand_control_reverse[JCQ_CONTROL].keys()
)
# Reverse the score, so that the maximum value becomes the minimum etc.
limesurvey_control.loc[rows_control_reverse, "score"] = (
LIMESURVEY_JCQ_MAX
+ LIMESURVEY_JCQ_MIN
- limesurvey_control.loc[rows_control_reverse, "score_original"]
)
pd.concat([baseline_interim, limesurvey_control], axis=0, ignore_index=True)
if "control" in requested_features:
baseline_features.loc[0, "limesurvey_control"] = limesurvey_control[
"score"
].sum()
if "demand_control_ratio" in requested_features:
limesurvey_demand_control_ratio = (
limesurvey_demand["score"].sum() / limesurvey_control["score"].sum()
)
if (
JCQ_NORMS[participant_info.loc[0, "gender"]][0]
<= limesurvey_demand_control_ratio
< JCQ_NORMS[participant_info.loc[0, "gender"]][1]
):
limesurvey_quartile = 1
elif (
JCQ_NORMS[participant_info.loc[0, "gender"]][1]
<= limesurvey_demand_control_ratio
< JCQ_NORMS[participant_info.loc[0, "gender"]][2]
):
limesurvey_quartile = 2
elif (
JCQ_NORMS[participant_info.loc[0, "gender"]][2]
<= limesurvey_demand_control_ratio
< JCQ_NORMS[participant_info.loc[0, "gender"]][3]
):
limesurvey_quartile = 3
elif (
JCQ_NORMS[participant_info.loc[0, "gender"]][3]
<= limesurvey_demand_control_ratio
< JCQ_NORMS[participant_info.loc[0, "gender"]][4]
):
limesurvey_quartile = 4
else:
limesurvey_quartile = np.nan
baseline_features.loc[
0, "limesurvey_demand_control_ratio"
] = limesurvey_demand_control_ratio
baseline_features.loc[
0, "limesurvey_demand_control_ratio_quartile"
] = limesurvey_quartile
if not baseline_interim.empty:
baseline_interim.to_csv(snakemake.output["interim"], index=False, encoding="utf-8")
baseline_features.to_csv(snakemake.output["features"], index=False, encoding="utf-8")