junos
/
rapids
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Cleaning script corrections

notes
Primoz 2022-09-27 14:12:08 +00:00
parent f0b87c9dd0
commit 536b9494cd
2 changed files with 51 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
data/external/participant_files/p01.yaml vendored
View File

@ -1,9 +1,9 @@
PHONE:
DEVICE_IDS: [70cc5183-97d4-4678-b81e-a34e491e2868,d5bbb2ab-2d60-4e72-a636-17655395c401,93fae5bc-e5a9-4751-b768-fd55c821f126]
PLATFORMS: [android,android,android]
LABEL: uploader_57312
START_DATE: 2020-09-24 11:56:45
END_DATE: 2020-10-24 19:19:37
DEVICE_IDS: [4b62a655-cbf0-4ac0-a448-06726f45b56a]
PLATFORMS: [android]
LABEL: uploader_53573
START_DATE: 2021-05-21 09:21:24
END_DATE: 2021-07-12 17:32:07
EMPATICA:
DEVICE_IDS: [empatica1]
LABEL: test01

77
src/features/all_cleaning_individual/straw/main.py
View File

@ -23,14 +23,11 @@ def straw_cleaning(sensor_data_files, provider):
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(drop=True)
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(drop=True)
# (2) 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]]
# (3.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
# (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
@ -40,10 +37,10 @@ def straw_cleaning(sensor_data_files, provider):
raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]]
features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]]
features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
# ---> imputation ??
@ -67,57 +64,75 @@ def straw_cleaning(sensor_data_files, provider):
# print(features[features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']][phone_cols])
# (3.2) (optional) DOES ROW CONSIST OF ENOUGH NON-NAN VALUES? Possible some of these examples could still pass previous condition but not this one?
# (2.2) (optional) DOES ROW CONSIST OF ENOUGH NON-NAN VALUES? Possible some of these examples could still pass previous condition but not this one?
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)
# (4) 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
graph_bf_af(features, "before_knn")
# (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)
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
impute_cols = [col for col in features.columns if col not in excluded_columns]
features[impute_cols] = impute(features[impute_cols], method="knn")
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
graph_bf_af(features, "after_knn")
# (5) REMOVE COLS WHERE VARIANCE IS 0
if provider["COLS_VAR_THRESHOLD"]:
features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
# 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]
graph_bf_af(features, "before_knn")
## (4) 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
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
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")
# (6) DROP HIGHLY CORRELATED FEATURES
# (7) DROP HIGHLY CORRELATED FEATURES
drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
if drop_corr_features["COMPUTE"]:
if drop_corr_features["COMPUTE"] and features.shape[0] >= 3:
numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
# Remove columns where NaN count threshold is passed
valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
cor_matrix = valid_features.corr(method='spearman').abs()
upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
corr_matrix = valid_features.corr().abs()
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
features.drop(to_drop, axis=1, inplace=True)
## (8) STANDARDIZATION
if provider["STANDARDIZATION"]:
features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
graph_bf_af(features, "after_corr")
# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
# 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
if features.isna().any().any():
raise ValueError
sys.exit()
return features
def graph_bf_af(features, phase_name):
sns.set(rc={"figure.figsize":(16, 8)})
print(features)
sns.heatmap(features.isna(), cbar=False)
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'):