Disable discarding rows if DATA_YIELD_RATIO_THRESHOLD==0.
Primoz
parent
607da820f2
commit
4cfe5a3a98
16
config.yaml
16
config.yaml
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@ -3,7 +3,7 @@
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########################################################################################################################
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# See https://www.rapids.science/latest/setup/configuration/#participant-files
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PIDS: ['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']
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PIDS: ['p01']
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# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
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CREATE_PARTICIPANT_FILES:
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@ -685,14 +685,14 @@ ALL_CLEANING_INDIVIDUAL:
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CORR_THRESHOLD: 0.95
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SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
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STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
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COMPUTE: False
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COMPUTE: True
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IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
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COMPUTE: False
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TYPE: median # options: zero, mean, median or k-nearest
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MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
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COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
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COLS_VAR_THRESHOLD: True
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ROWS_NAN_THRESHOLD: 0 # set to 1 to disable
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ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
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DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
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DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
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DROP_HIGHLY_CORRELATED_FEATURES:
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@ -705,7 +705,7 @@ ALL_CLEANING_OVERALL:
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CLEAN_STANDARDIZED: True
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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IMPUTE_SELECTED_EVENT_FEATURES:
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COMPUTE: False
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MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
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@ -720,14 +720,14 @@ ALL_CLEANING_OVERALL:
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CORR_THRESHOLD: 0.95
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SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
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STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
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COMPUTE: False
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COMPUTE: True
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IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
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COMPUTE: False
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TYPE: median # options: zero, mean, median or k-nearest
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MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
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COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
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COLS_VAR_THRESHOLD: True
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ROWS_NAN_THRESHOLD: 0 # set to 1 to disable
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ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
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DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
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DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
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DROP_HIGHLY_CORRELATED_FEATURES:
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@ -759,7 +759,7 @@ STANDARDIZATION: # Standardization for both providers is executed if only one of
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PARAMS_FOR_ANALYSIS:
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BASELINE:
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COMPUTE: True
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COMPUTE: False
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FOLDER: data/external/baseline
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CONTAINER: [results-survey637813_final.csv, # Slovenia
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results-survey358134_final.csv, # Belgium 1
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@ -770,5 +770,5 @@ PARAMS_FOR_ANALYSIS:
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CATEGORICAL_FEATURES: [gender]
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TARGET:
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COMPUTE: True
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COMPUTE: False
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LABEL: PANAS_negative_affect_mean
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@ -39,8 +39,10 @@ rapids_cleaning <- function(sensor_data_files, provider){
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if(!data_yield_column %in% colnames(clean_features)){
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stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
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}
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clean_features <- clean_features %>%
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if (data_yield_ratio_threshold > 0) {
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clean_features <- clean_features %>%
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filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
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}
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# Drop columns with a percentage of NA values above cols_nan_threshold
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if(nrow(clean_features))
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@ -36,7 +36,8 @@ def straw_cleaning(sensor_data_files, provider):
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if not data_yield_column in features.columns:
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raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
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features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
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if provider["DATA_YIELD_RATIO_THRESHOLD"]:
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features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
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esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
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@ -39,8 +39,10 @@ rapids_cleaning <- function(sensor_data_files, provider){
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if(!data_yield_column %in% colnames(clean_features)){
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stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
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}
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clean_features <- clean_features %>%
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if (data_yield_ratio_threshold > 0) {
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clean_features <- clean_features %>%
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filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
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}
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# Drop columns with a percentage of NA values above cols_nan_threshold
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if(nrow(clean_features))
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@ -36,7 +36,8 @@ def straw_cleaning(sensor_data_files, provider):
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if not data_yield_column in features.columns:
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raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
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features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
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if provider["DATA_YIELD_RATIO_THRESHOLD"]:
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features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
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esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
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