Merge branch 'imputation_and_cleaning' of https://repo.ijs.si/junoslukan/rapids into imputation_and_cleaning
Primoz
commit
7fcdb873fe
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@ -670,10 +670,10 @@ ALL_CLEANING_INDIVIDUAL:
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MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
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COLS_NAN_THRESHOLD: 0.9 # set to 1 remove only columns that contains all NaN
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COLS_VAR_THRESHOLD: True
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ROWS_NAN_THRESHOLD: 0.7 # set to 1 to disable
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ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
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PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
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PHONE_DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
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EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
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PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.4 # set to 0 to disable
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EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.25 # set to 0 to disable
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DROP_HIGHLY_CORRELATED_FEATURES:
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COMPUTE: True
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MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
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@ -709,6 +709,7 @@ ALL_CLEANING_OVERALL:
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ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
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PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
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PHONE_DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
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EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
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DROP_HIGHLY_CORRELATED_FEATURES:
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COMPUTE: True
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MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
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@ -8,6 +8,8 @@ from sklearn.preprocessing import StandardScaler
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import matplotlib.pyplot as plt
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import seaborn as sns
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from src.features import empatica_data_yield as edy
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def straw_cleaning(sensor_data_files, provider):
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features = pd.read_csv(sensor_data_files["sensor_data"][0])
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@ -19,68 +21,52 @@ def straw_cleaning(sensor_data_files, provider):
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excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
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# (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
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if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
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target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
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features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
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# TODO: imputate the rows where the participants have at least 2 rows (2 time segments) - error prevention (has to be tested)
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# TODO: because of different imputation logic (e.g., the phone_data_yield parameter for phone features) the imputation has to
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# be planned accordingly. Should the phone features first be imputated with 0 and only then general kNN imputation is executed
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# i.e., on the rows that are missing when E4 and phone features availability is not synchronized. CHECK phone_data_yield feat.
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# A lot of imputation types/levels (1) imputation related to feature's content (2) imputation related to phone / empatica
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# structual specifics (3) general imputation which is needed when types of features desynchronization is present (row is not full)
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# because of the lack of the availability. Secondly, there's a high importance that features data frame is checked if and NaN
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# values still exist.
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# (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)
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features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
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# (3.1) QUALITY CHECK (DATA YIELD COLUMN) which determines if the row stays or not (if either E4 or phone is low quality the row is useless - TODO: determine threshold)
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# Here, the imputation is still not executed - only quality check
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# ??? Drop rows with the value of phone_data_yield_column less than data_yield_ratio_threshold ???
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phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
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phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
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empatica_data_yield_column = "????????????"
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features = edy.calculate_empatica_data_yield(features)
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if not phone_data_yield_column in features.columns and not empatica_data_yield_column in features.columns:
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if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
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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].")
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if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
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features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]]
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# Potrebno premisliti točno kako bi izgledal data_yield za E4: bo se ustvaril dodaten stolpec; bodo različne spremenljivke, podobno kot hour in minute pri phone?
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if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
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features = features[features['???????????????'] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]]
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features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]]
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# ---> imputation ??
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impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
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# impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
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if True: #impute_phone_features["COMPUTE"]:
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if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
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raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
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# TODO: if the type of the imputation will vary for different groups of features make conditional imputations here
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phone_cols = [col for col in features if \
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col.startswith('phone_applications_foreground_rapids_') or
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col.startswith('phone_battery_rapids_') or
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col.startswith('phone_calls_rapids_') or
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col.startswith('phone_keyboard_rapids_') or
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col.startswith('phone_messages_rapids_') or
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col.startswith('phone_screen_rapids_') or
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col.startswith('phone_wifi_')]
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# if True: #impute_phone_features["COMPUTE"]:
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# if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
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# raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
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mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
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features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
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# phone_cols = [col for col in features if \
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# col.startswith('phone_applications_foreground_rapids_') or
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# col.startswith('phone_battery_rapids_') or
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# col.startswith('phone_calls_rapids_') or
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# col.startswith('phone_keyboard_rapids_') or
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# col.startswith('phone_messages_rapids_') or
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# col.startswith('phone_screen_rapids_') or
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# col.startswith('phone_wifi_')]
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print(features[features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']][phone_cols])
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# mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
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# features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
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# print(features[features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']][phone_cols])
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# (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?
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# () Remove rows if threshold of NaN values is passed
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min_count = math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
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features.dropna(axis=0, thresh=min_count, inplace=True)
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@ -118,11 +104,9 @@ def straw_cleaning(sensor_data_files, provider):
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features.drop(to_drop, axis=1, inplace=True)
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## (8) STANDARDIZATION
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if provider["STANDARDIZATION"]:
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features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
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# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
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if features.isna().any().any():
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raise ValueError
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@ -10,17 +10,19 @@ def calculate_empatica_data_yield(features):
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datetime_end = datetime.strptime(df.loc[0, 'local_segment_end_datetime'], '%y-%m-%d %H:%M:%S')
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tseg_duration = (datetime_end - datetime_start).total_seconds()
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acc_data_yield = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration
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temp_data_yield = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration
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acc_data_yield = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration
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ibi_data_yield = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration
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features["acc_data_yield"] = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration
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features["temp_data_yield"] = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration
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features["eda_data_yield"] = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration
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features["ibi_data_yield"] = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration
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features["empatica_data_yield"] = features[['acc_data_yield', 'temp_data_yield', 'eda_data_yield', 'ibi_data_yield']].mean(axis=1)
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# TODO: morda smisleno obdelovati različne senzorje ločeno -> lahko da ibi ne bo dobre kvalitete, ostali pa bodo okej. Zakaj bi samo zaradi IBI zavrgli celotno vrstico ...
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# lahko se tudi naredi overall kvaliteta empatice npr. povprečje vseh data_yield rezultatov? Oz. povprečje z utežmi glede na število stolpcev, ki jih senzor vsebuje
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# ... čeprav št. stolpcev ni najboljše, saj je pomembnost nekaterih (npr. EDA) značilk zelo vprašljiva.
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# TODO: bolja nastavitev delovnih ur sedaj je od 4 do 4... to povzroči veliko manjkajočih podatkov in posledično nizek (telefonski in E4) data_yield ...
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# TODO: boljša nastavitev delovnih ur sedaj je od 4 do 4... to povzroči veliko manjkajočih podatkov in posledično nizek (telefonski in E4) data_yield ...
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data_yield_features = [col for col in features.columns if "SO_windowsCount" in col and "a"]
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return features
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