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Reorganisation and reordering of the cleaning script.

notes
Primoz 2022-09-12 13:44:17 +00:00
parent 15d792089d
commit d27a4a71c8
2 changed files with 33 additions and 13 deletions
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4
config.yaml
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@ -688,7 +688,7 @@ ALL_CLEANING_INDIVIDUAL:
COMPUTE: True
IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
COMPUTE: False
TYPE: median # options: zero, mean, median or k-nearest
TYPE: zero # options: zero, mean, median or k-nearest
MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
COLS_NAN_THRESHOLD: 1 # set to 1 remove only columns that contains all NaN
COLS_VAR_THRESHOLD: True
@ -723,7 +723,7 @@ ALL_CLEANING_OVERALL:
COMPUTE: True
IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
COMPUTE: False
TYPE: median # options: zero, mean, median or k-nearest
TYPE: zero # options: zero, mean, median or k-nearest
MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
COLS_NAN_THRESHOLD: 1 # set to 1 remove only columns that contains all NaN
COLS_VAR_THRESHOLD: True

42
src/features/all_cleaning_individual/straw/main.py
View File

@ -4,25 +4,26 @@ import math, sys
import yaml
from sklearn.impute import KNNImputer
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
def straw_cleaning(sensor_data_files, provider):
features = pd.read_csv(sensor_data_files["sensor_data"][0])
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
with open('config.yaml', 'r') as stream:
config = yaml.load(stream, Loader=yaml.FullLoader)
#Filter-out all rows that do not have the target column available
# (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()
test_cols = [col for col in features.columns if 'phone_calls' in col or 'phone_messages' in col]
# TODO: reorder the cleaning steps so it makes sense for the analysis
# TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
# the snakemake rules will also have to come with additional parameter (in rules/features.smk)
@ -36,7 +37,7 @@ def straw_cleaning(sensor_data_files, provider):
# because of the lack of the availability. Secondly, there's a high importance that features data frame is checked if and NaN
# values still exist.
# Impute selected features event
# (2) PARTIAL IMPUTATION: IMPUTE DATA DEPENDEND ON THE FEATURES GROUP (e.g., phone or E4 features)
impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
if impute_phone_features["COMPUTE"]:
if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
@ -55,7 +56,7 @@ def straw_cleaning(sensor_data_files, provider):
mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
# Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
# ??? Drop rows with the value of data_yield_column less than data_yield_ratio_threshold ???
data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
@ -65,10 +66,10 @@ def straw_cleaning(sensor_data_files, provider):
if provider["DATA_YIELD_RATIO_THRESHOLD"]:
features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
# Remove cols if threshold of NaN values is passed (should be <= if even all NaN columns must be preserved)
# (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved)
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
# Remove cols where variance is 0
# (4) REMOVE COLS WHERE VARIANCE IS 0
if provider["COLS_VAR_THRESHOLD"]:
features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
@ -77,7 +78,7 @@ def straw_cleaning(sensor_data_files, provider):
if esm not in features:
features[esm] = esm_cols[esm]
# Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
# (5) DROP HIGHLY CORRELATED FEATURES
drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
if drop_corr_features["COMPUTE"]:
@ -98,7 +99,26 @@ def straw_cleaning(sensor_data_files, provider):
sns.set(rc={"figure.figsize":(16, 8)})
sns.heatmap(features.isna(), cbar=False)
plt.savefig(f'features_nans.png', bbox_inches='tight')
plt.savefig(f'features_nans_bf_knn.png', bbox_inches='tight')
# KNN IMPUTATION
features = impute(features, method="knn")
sns.set(rc={"figure.figsize":(16, 8)})
sns.heatmap(features.isna(), cbar=False)
plt.savefig(f'features_nans_af_knn.png', bbox_inches='tight')
## STANDARDIZATION - should it happen before or after kNN imputation?
# TODO: check if there are additional columns that need to be excluded from the standardization
excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
excluded_columns += [col for col in features.columns if "level_1" in col]
features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
# VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
if features.isna.any().any():
raise ValueError
sys.exit()
@ -106,14 +126,14 @@ def straw_cleaning(sensor_data_files, provider):
def impute(df, method='zero'):
def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
def k_nearest(df):
imputer = KNNImputer(n_neighbors=3)
return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
return { # rest of the columns should be imputed with the selected method
return {
'zero': df.fillna(0),
'mean': df.fillna(df.mean()),
'median': df.fillna(df.median()),
'k-nearest': k_nearest(df)
'knn': k_nearest(df)
}[method]