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Extend imputation logic within the cleaning script.

imputation_and_cleaning
Primoz 2022-11-02 14:01:21 +00:00
parent 30b38bfc02
commit 0ce8723bdb
1 changed files with 19 additions and 9 deletions
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28
src/features/all_cleaning_overall/straw/main.py
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@ -96,18 +96,29 @@ def straw_cleaning(sensor_data_files, provider, target):
impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col] impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - loglocation features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - loglocation
# Impute selected phone features with 0 + impute ESM features with 0 # Impute location features
impute_locations = [col for col in features \
if col.startswith('phone_locations_doryab_') and
'radiusgyration' not in col
]
# Impute selected phone, location, and esm features with 0
impute_zero = [col for col in features if \ impute_zero = [col for col in features if \
col.startswith('phone_applications_foreground_rapids_') or col.startswith('phone_applications_foreground_rapids_') or
col.startswith('phone_activity_recognition_') or
col.startswith('phone_battery_rapids_') or col.startswith('phone_battery_rapids_') or
col.startswith('phone_bluetooth_rapids_') or col.startswith('phone_bluetooth_rapids_') or
col.startswith('phone_light_rapids_') or col.startswith('phone_light_rapids_') or
col.startswith('phone_calls_rapids_') or col.startswith('phone_calls_rapids_') or
col.startswith('phone_messages_rapids_') or col.startswith('phone_messages_rapids_') or
col.startswith('phone_screen_rapids_') or col.startswith('phone_screen_rapids_') or
col.startswith('phone_wifi_visible')] col.startswith('phone_bluetooth_doryab_') or
col.startswith('phone_wifi_visible')
features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0) ]
features[impute_zero+impute_locations+list(esm_cols.columns)] = features[impute_zero+impute_locations+list(esm_cols.columns)].fillna(0)
pd.set_option('display.max_rows', None)
graph_bf_af(features, "4context_imp") graph_bf_af(features, "4context_imp")
@ -138,15 +149,14 @@ def straw_cleaning(sensor_data_files, provider, target):
if features.empty: if features.empty:
return pd.DataFrame(columns=excluded_columns) return pd.DataFrame(columns=excluded_columns)
# (7) STANDARDIZATION
# (7) STANDARDIZATION TODO: exclude nominal features from standardization
if provider["STANDARDIZATION"]: if provider["STANDARDIZATION"]:
nominal_cols = [col for col in features.columns if "mostcommonactivity" in col or "homelabel" in col] # Excluded nominal features
# Expected warning within this code block # Expected warning within this code block
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning) warnings.simplefilter("ignore", category=RuntimeWarning)
features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \ features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols)] = \
features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel()) features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
graph_bf_af(features, "8standardization") graph_bf_af(features, "8standardization")