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Contextual imputation by feature type.

notes
Primoz 2022-09-28 10:02:47 +00:00
parent 536b9494cd
commit 8af4ef11dc
2 changed files with 41 additions and 23 deletions
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6
config.yaml
View File

@ -3,7 +3,7 @@
########################################################################################################################
# See https://www.rapids.science/latest/setup/configuration/#participant-files
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']
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']
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
@ -159,7 +159,7 @@ PHONE_BLUETOOTH:
CONTAINER: bluetooth
PROVIDERS:
RAPIDS:
COMPUTE: True
COMPUTE: False
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
@ -668,7 +668,7 @@ ALL_CLEANING_INDIVIDUAL:
COMPUTE: False
TYPE: zero # options: zero, mean, median or k-nearest
MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
COLS_NAN_THRESHOLD: 0.9 # set to 1 remove only columns that contains all NaN
COLS_NAN_THRESHOLD: 1 # set to 1 remove only columns that contains all NaN
COLS_VAR_THRESHOLD: True
ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES

58
src/features/all_cleaning_individual/straw/main.py
View File

@ -78,29 +78,48 @@ def straw_cleaning(sensor_data_files, provider):
if esm not in features:
features[esm] = esm_cols[esm]
graph_bf_af(features, "before_knn")
# (4) CONTEXTUAL IMPUTATION
## (4) STANDARDIZATION
graph_bf_af(features, "contextual_imputation_before")
# Impute selected phone features with a high number
impute_w_hn = [col for col in features.columns if \
"timeoffirstuse" in col or
"timeoflastuse" in col or
"timefirstcall" in col or
"timelastcall" in col or
"timefirstmessages" in col or
"timelastmessages" in col or
"firstuseafter" in col]
features[impute_w_hn] = impute(features[impute_w_hn], method="high_number")
# Impute phone locations with median
impute_locations = [col for col in features.columns if "phone_locations_" in col]
features[impute_locations] = impute(features[impute_locations], method="median")
# Impute remaining phone features with 0
impute_rest = [col for col in features.columns if "phone_" in col]
features[impute_locations] = impute(features[impute_locations], method="zero")
graph_bf_af(features, "contextual_imputation_after")
## (5) STANDARDIZATION
if provider["STANDARDIZATION"]:
features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
# (5) IMPUTATION: IMPUTE DATA WITH KNN METHOD (TODO: for now only kNN)
# - no other input restriction for this method except that rows are full enough and have reasonably high quality as assessed by data yield
# (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
impute_cols = [col for col in features.columns if col not in excluded_columns]
features[impute_cols] = impute(features[impute_cols], method="knn")
graph_bf_af(features, "after_knn")
# (6) REMOVE COLS WHERE VARIANCE IS 0
# (7) REMOVE COLS WHERE VARIANCE IS 0
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
if provider["COLS_VAR_THRESHOLD"]:
features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
graph_bf_af(features, "before_corr")
# (7) DROP HIGHLY CORRELATED FEATURES
# (8) DROP HIGHLY CORRELATED FEATURES
drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
if drop_corr_features["COMPUTE"] and features.shape[0] >= 3:
if drop_corr_features["COMPUTE"] and features.shape[0] > 5: # If small amount of segments (rows) is present, do not execute correlation check
numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
@ -113,15 +132,12 @@ def straw_cleaning(sensor_data_files, provider):
features.drop(to_drop, axis=1, inplace=True)
graph_bf_af(features, "after_corr")
# Preserve esm cols if deleted (has to come after drop cols operations)
for esm in esm_cols:
if esm not in features:
features[esm] = esm_cols[esm]
# (8) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
if features.isna().any().any():
raise ValueError
@ -129,12 +145,6 @@ def straw_cleaning(sensor_data_files, provider):
return features
def graph_bf_af(features, phase_name):
sns.set(rc={"figure.figsize":(16, 8)})
print(features)
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
plt.savefig(f'features_nans_{phase_name}.png', bbox_inches='tight')
def impute(df, method='zero'):
def k_nearest(df):
@ -143,8 +153,16 @@ def impute(df, method='zero'):
return {
'zero': df.fillna(0),
'high_number': df.fillna(1000000),
'mean': df.fillna(df.mean()),
'median': df.fillna(df.median()),
'knn': k_nearest(df)
}[method]
def graph_bf_af(features, phase_name):
sns.set(rc={"figure.figsize":(16, 8)})
print(features)
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
plt.savefig(f'features_nans_{phase_name}.png', bbox_inches='tight')