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Merge branch 'imputation_and_cleaning' of https://repo.ijs.si/junoslukan/rapids into imputation_and_cleaning

notes
Primoz 2022-09-21 07:18:01 +00:00
parent 90ee99e4b9 7493aaa643
commit 247d758cb7
6 changed files with 30 additions and 33 deletions
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8
Snakefile
View File

@ -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
View File

@ -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
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@ -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"

27
src/features/all_cleaning_individual/straw/main.py
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@ -17,12 +17,13 @@ def straw_cleaning(sensor_data_files, provider):
with open('config.yaml', 'r') as stream:
config = yaml.load(stream, Loader=yaml.FullLoader)
excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
# (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
features = features[features['phone_esm_straw_' + target].notna()].reset_index()
features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
# TODO: reorder the cleaning steps so it makes sense for the analysis
# TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
# the snakemake rules will also have to come with additional parameter (in rules/features.smk)
@ -64,10 +65,10 @@ def straw_cleaning(sensor_data_files, provider):
if provider["DATA_YIELD_RATIO_THRESHOLD"]:
features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
# (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved)
# (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
# (4) REMOVE COLS WHERE VARIANCE IS 0 TODO: preveri za local_segment stolpce
# (4) REMOVE COLS WHERE VARIANCE IS 0
if provider["COLS_VAR_THRESHOLD"]:
features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
@ -91,32 +92,36 @@ def straw_cleaning(sensor_data_files, provider):
features.drop(to_drop, axis=1, inplace=True)
# Remove rows if threshold of NaN values is passed
# (6) Remove rows if threshold of NaN values is passed
min_count = math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
features.dropna(axis=0, thresh=min_count, inplace=True)
sns.set(rc={"figure.figsize":(16, 8)})
sns.heatmap(features.isna(), cbar=False)
plt.savefig(f'features_nans_bf_knn.png', bbox_inches='tight')
## STANDARDIZATION - should it happen before or after kNN imputation?
# TODO: check if there are additional columns that need to be excluded from the standardization
excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
## (7) STANDARDIZATION
if provider["STANDARDIZATION"]:
features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
# KNN IMPUTATION
# (8) KNN IMPUTATION
impute_cols = [col for col in features.columns if col not in excluded_columns]
features[impute_cols] = impute(features[impute_cols], method="knn")
# (9) STANDARDIZATION AGAIN
if provider["STANDARDIZATION"]:
features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
sns.set(rc={"figure.figsize":(16, 8)})
sns.heatmap(features.isna(), cbar=False)
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():
# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
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)
}

4
tests/scripts/missing_vals.py
View File

@ -3,8 +3,8 @@ import seaborn as sns
import matplotlib.pyplot as plt
participant = "p031"
all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]
participant = "p01"
all_sensors = ["eda", "ibi", "temp", "acc"]
for sensor in all_sensors: