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Small imputation and cleaning corrections.

notes
Primoz 2022-09-20 08:03:48 +00:00
parent a96ea508c6
commit eaf4340afd
5 changed files with 13 additions and 21 deletions
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12
Snakefile
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@ -410,14 +410,14 @@ for provider in config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"].keys():
if config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][provider]["COMPUTE"]:
if provider == "STRAW":
files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
else:
else:
files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
if provider == "STRAW":
files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
else:
else:
files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
# Baseline features
@ -429,10 +429,10 @@ if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
# Targets (labels)
if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
# files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
# files_to_compute.extend(expand("data/processed/models/population_model/input.csv"))
files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/z_input.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/processed/models/population_model/z_input.csv"))
files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/processed/models/population_model/input.csv"))
# files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/z_input.csv", pid=config["PIDS"]))
# files_to_compute.extend(expand("data/processed/models/population_model/z_input.csv"))
#files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"]))

3
config.yaml
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@ -3,7 +3,7 @@
########################################################################################################################
# See https://www.rapids.science/latest/setup/configuration/#participant-files
PIDS: ['p01'] #['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']
PIDS: ['p01', 'p02'] #['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
@ -70,7 +70,6 @@ PHONE_ACCELEROMETER:
COMPUTE: False
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
SRC_SCRIPT: src/features/phone_accelerometer/rapids/main.py
PANDA:
COMPUTE: False
VALID_SENSED_MINUTES: False

8
rules/models.smk
View File

@ -30,22 +30,22 @@ rule baseline_features:
rule select_target:
input:
cleaned_sensor_features = "data/processed/features/{pid}/z_all_sensor_features_cleaned_straw_py.csv"
cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_straw_py.csv"
params:
target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
output:
"data/processed/models/individual_model/{pid}/z_input.csv"
"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/z_all_sensor_features_cleaned_straw_py.csv",
cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_straw_py.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/z_input.csv"
"data/processed/models/population_model/input.csv"
script:
"../src/models/merge_features_and_targets_for_population_model.py"

2
src/features/all_cleaning_individual/straw/main.py
View File

@ -116,7 +116,7 @@ def straw_cleaning(sensor_data_files, provider):
plt.savefig(f'features_nans_af_knn.png', bbox_inches='tight')
# VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
if features.isna.any().any():
if features.isna().any().any():
raise ValueError
sys.exit()

9
src/features/phone_wifi_visible/rapids/main.R
View File

@ -23,13 +23,6 @@ compute_wifi_feature <- function(data, feature, time_segment){
data <- data %>% filter_data_by_segment(time_segment)
print(data %>%
filter(bssid == mostuniquedevice) %>%
group_by(local_segment) %>%
summarise(!!feature := n()))
raise
return(data %>%
filter(bssid == mostuniquedevice) %>%
group_by(local_segment) %>%
@ -55,6 +48,6 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
feature <- compute_wifi_feature(wifi_data, feature_name, time_segment)
features <- merge(features, feature, by="local_segment", all = TRUE)
}
# features <- features %>% mutate_all(~replace(., is.na(.), 0))
features <- features %>% mutate_all(~replace(., is.na(.), 0))
return(features)
}