E4 data yield corrections. Changes in overal cs - standardization.
Primoz
parent
437459648f
commit
a34412a18d
|
|
@ -680,7 +680,7 @@ ALL_CLEANING_INDIVIDUAL:
|
|||
ALL_CLEANING_OVERALL:
|
||||
PROVIDERS:
|
||||
RAPIDS:
|
||||
COMPUTE: True
|
||||
COMPUTE: False
|
||||
IMPUTE_SELECTED_EVENT_FEATURES:
|
||||
COMPUTE: False
|
||||
MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
|
||||
|
|
|
|||
|
|
@ -1,10 +1,9 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import math, sys, random
|
||||
import yaml
|
||||
import math, sys, random, warnings, yaml
|
||||
|
||||
from sklearn.impute import KNNImputer
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.preprocessing import StandardScaler, minmax_scale
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
|
||||
|
|
@ -31,7 +30,7 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
|
||||
graph_bf_af(features, "2target_rows_after")
|
||||
|
||||
# (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
|
||||
# (2) QUALITY CHECK (DATA YIELD COLUMN) drops 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
|
||||
|
||||
|
|
@ -43,42 +42,26 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
hist = features[["empatica_data_yield", phone_data_yield_column]].hist()
|
||||
plt.legend()
|
||||
plt.savefig(f'phone_E4_histogram.png', bbox_inches='tight')
|
||||
|
||||
# Drop rows where phone data yield is less then given threshold
|
||||
if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
|
||||
print("\nThreshold:", provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"])
|
||||
print("Phone features data yield stats:", features[phone_data_yield_column].describe(), "\n")
|
||||
print(features[phone_data_yield_column].sort_values())
|
||||
# print(features[phone_data_yield_column].sort_values())
|
||||
hist = features[phone_data_yield_column].hist(bins=5)
|
||||
plt.close()
|
||||
features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
|
||||
|
||||
# Drop rows where empatica data yield is less then given threshold
|
||||
if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
|
||||
print("\nThreshold:", provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"])
|
||||
print("E4 features data yield stats:", features["empatica_data_yield"].describe(), "\n")
|
||||
print(features["empatica_data_yield"].sort_values())
|
||||
# print(features["empatica_data_yield"].sort_values())
|
||||
features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
|
||||
|
||||
graph_bf_af(features, "3data_yield_drop_rows")
|
||||
|
||||
# (2.2) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
|
||||
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) # Thresh => at least this many not-nans
|
||||
|
||||
graph_bf_af(features, "4too_much_nans_rows")
|
||||
|
||||
# (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
|
||||
|
||||
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
|
||||
|
||||
graph_bf_af(features, "5too_much_nans_cols")
|
||||
|
||||
# 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]
|
||||
|
||||
# (4) CONTEXTUAL IMPUTATION
|
||||
# (3) CONTEXTUAL IMPUTATION
|
||||
|
||||
# Impute selected phone features with a high number
|
||||
impute_w_hn = [col for col in features.columns if \
|
||||
|
|
@ -91,7 +74,7 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
"timelastmessages" in col]
|
||||
features[impute_w_hn] = impute(features[impute_w_hn], method="high_number")
|
||||
|
||||
graph_bf_af(features, "6high_number_imp")
|
||||
graph_bf_af(features, "4high_number_imp")
|
||||
|
||||
# Impute special case (mostcommonactivity) and (homelabel)
|
||||
impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
|
||||
|
|
@ -112,7 +95,7 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
col.startswith('phone_wifi_visible')]
|
||||
features[impute_zero] = impute(features[impute_zero], method="zero")
|
||||
|
||||
graph_bf_af(features, "7zero_imp")
|
||||
graph_bf_af(features, "5zero_imp")
|
||||
|
||||
# Impute phone locations with median - should this rather be imputed at kNN step??
|
||||
# impute_locations = [col for col in features.columns if "phone_locations_" in col]
|
||||
|
|
@ -127,30 +110,53 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
# graph_bf_af(features[impute_locations], "phoneloc_before")
|
||||
|
||||
# features[impute_locations] = features[impute_locations + ["pid"]].groupby("pid").transform(lambda x: x.fillna(x.median()))[impute_locations]
|
||||
|
||||
# (4) 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
|
||||
|
||||
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
|
||||
|
||||
# (5) STANDARDIZATION
|
||||
if provider["STANDARDIZATION"]:
|
||||
features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \
|
||||
features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: 0 if (x.std() == 0) else (x - x.mean()) / x.std())
|
||||
graph_bf_af(features, "6too_much_nans_cols")
|
||||
|
||||
graph_bf_af(features, "8standardization")
|
||||
|
||||
# (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
|
||||
impute_cols = [col for col in features.columns if col not in excluded_columns and col != "pid"]
|
||||
features[impute_cols] = impute(features[impute_cols], method="knn")
|
||||
|
||||
graph_bf_af(features, "9knn_after")
|
||||
|
||||
# (7) REMOVE COLS WHERE VARIANCE IS 0
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
|
||||
# (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)
|
||||
|
||||
graph_bf_af(features, "10variance_drop")
|
||||
graph_bf_af(features, "7variance_drop")
|
||||
|
||||
# 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]
|
||||
|
||||
# (6) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
|
||||
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) # Thresh => at least this many not-nans
|
||||
|
||||
graph_bf_af(features, "8too_much_nans_rows")
|
||||
|
||||
# (7) STANDARDIZATION
|
||||
# I expect to see RuntimeWarnings in this block
|
||||
if provider["STANDARDIZATION"]:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore", category=RuntimeWarning)
|
||||
features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \
|
||||
features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: minmax_scale(x.astype(float)))
|
||||
|
||||
graph_bf_af(features, "9standardization")
|
||||
|
||||
# (8) IMPUTATION: IMPUTE DATA WITH KNN METHOD
|
||||
features.reset_index(drop=True, inplace=True)
|
||||
impute_cols = [col for col in features.columns if col not in excluded_columns and col != "pid"]
|
||||
features[impute_cols] = impute(features[impute_cols], method="knn")
|
||||
|
||||
graph_bf_af(features, "10knn_after")
|
||||
|
||||
|
||||
# (9) DROP HIGHLY CORRELATED FEATURES
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
|
||||
|
||||
# (8) DROP HIGHLY CORRELATED FEATURES
|
||||
drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
|
||||
if drop_corr_features["COMPUTE"] and features.shape[0] > 5: # If small amount of segments (rows) is present, do not execute correlation check
|
||||
|
||||
|
|
@ -163,6 +169,11 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
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"])]
|
||||
|
||||
sns.heatmap(corr_matrix, cmap="YlGnBu", annot=True)
|
||||
plt.savefig(f'correlation_matrix.png', bbox_inches='tight')
|
||||
plt.close()
|
||||
# TODO: katere značilke se izbrišejo - ali korelirajo kakšni pari E4:PHONE?
|
||||
|
||||
features.drop(to_drop, axis=1, inplace=True)
|
||||
|
||||
# Preserve esm cols if deleted (has to come after drop cols operations)
|
||||
|
|
@ -172,10 +183,11 @@ def straw_cleaning(sensor_data_files, provider):
|
|||
|
||||
graph_bf_af(features, "11correlation_drop")
|
||||
|
||||
# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
|
||||
# (10) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
|
||||
if features.isna().any().any():
|
||||
raise ValueError
|
||||
raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
|
||||
|
||||
sys.exit()
|
||||
return features
|
||||
|
||||
def impute(df, method='zero'):
|
||||
|
|
@ -192,7 +204,7 @@ def impute(df, method='zero'):
|
|||
'knn': k_nearest(df)
|
||||
}[method]
|
||||
|
||||
def graph_bf_af(features, phase_name, plt_flag=False):
|
||||
def graph_bf_af(features, phase_name, plt_flag=True):
|
||||
if plt_flag:
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
|
||||
|
|
@ -201,5 +213,5 @@ def graph_bf_af(features, phase_name, plt_flag=False):
|
|||
print(f"\n-------------{phase_name}-------------")
|
||||
print("Rows number:", features.shape[0])
|
||||
print("Columns number:", len(features.columns))
|
||||
print("NaN values:", features.isna().sum().sum())
|
||||
print("---------------------------------------------\n")
|
||||
|
||||
|
|
|
|||
|
|
@ -10,12 +10,15 @@ def calculate_empatica_data_yield(features):
|
|||
datetime_end = datetime.strptime(features.loc[0, 'local_segment_end_datetime'], '%Y-%m-%d %H:%M:%S')
|
||||
tseg_duration = (datetime_end - datetime_start).total_seconds()
|
||||
|
||||
features["acc_data_yield"] = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration
|
||||
features["temp_data_yield"] = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration
|
||||
features["eda_data_yield"] = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration
|
||||
features["ibi_data_yield"] = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration
|
||||
features["acc_data_yield"] = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration \
|
||||
if 'empatica_accelerometer_cr_SO_windowsCount' in features else 0
|
||||
features["temp_data_yield"] = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration \
|
||||
if 'empatica_temperature_cr_SO_windowsCount' in features else 0
|
||||
features["eda_data_yield"] = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration \
|
||||
if 'empatica_electrodermal_activity_cr_SO_windowsCount' in features else 0
|
||||
features["ibi_data_yield"] = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration \
|
||||
if 'empatica_inter_beat_interval_cr_SO_windowsCount' in features else 0
|
||||
|
||||
# TODO: boljša nastavitev delovnih ur sedaj je od 4:00 do 4:00... to povzroči veliko manjkajočih podatkov in posledično nizek (telefonski in E4) data_yield ...
|
||||
empatica_data_yield_cols = ['acc_data_yield', 'temp_data_yield', 'eda_data_yield', 'ibi_data_yield']
|
||||
features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1).fillna(0)
|
||||
features.drop(empatica_data_yield_cols, axis=1, inplace=True) # In case of if the advanced operations will later not be needed (e.g., weighted average)
|
||||
|
|
|
|||
Loading…
Reference in New Issue