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

Code cleaning, reworking cleaning individual based on changes in overall script. Changes in thresholds.

notes
Primoz 2022-09-30 10:04:07 +00:00
parent 7ac7cd5a37
commit 44531c6d94
3 changed files with 32 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
config.yaml
View File

@ -667,7 +667,7 @@ ALL_CLEANING_INDIVIDUAL:
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.4 # set to 0 to disable
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.25 # set to 0 to disable
ROWS_NAN_THRESHOLD: 0.3 # set to 1 to disable
ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
COLS_VAR_THRESHOLD: True
DROP_HIGHLY_CORRELATED_FEATURES:
@ -699,8 +699,8 @@ ALL_CLEANING_OVERALL:
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.4 # set to 0 to disable
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.25 # set to 0 to disable
ROWS_NAN_THRESHOLD: 0.3 # set to 1 to disable
COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
COLS_VAR_THRESHOLD: True
DROP_HIGHLY_CORRELATED_FEATURES:
COMPUTE: True

28
src/features/all_cleaning_individual/straw/main.py
View File

@ -1,6 +1,6 @@
import pandas as pd
import numpy as np
import math, sys
import math, sys, random
import yaml
from sklearn.impute import KNNImputer
@ -11,6 +11,8 @@ import seaborn as sns
sys.path.append('/rapids/')
from src.features import empatica_data_yield as edy
pd.set_option('display.max_columns', 20)
def straw_cleaning(sensor_data_files, provider):
features = pd.read_csv(sensor_data_files["sensor_data"][0])
@ -66,26 +68,39 @@ def straw_cleaning(sensor_data_files, provider):
"timeoflastuse" in col or
"timefirstcall" in col or
"timelastcall" in col or
"firstuseafter" in col or
"timefirstmessages" in col or
"timelastmessages" in col]
features[impute_w_hn] = impute(features[impute_w_hn], method="high_number")
# Impute phone locations with median
impute_locations = [col for col in features.columns if "phone_locations_" in col]
features[impute_locations] = impute(features[impute_locations], method="median")
# Impute special case (mostcommonactivity)
impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation
# Impute remaining phone features with 0
impute_rest = [col for col in features.columns if "phone_" in col]
# Impute selected phone features with 0
impute_zero = [col for col in features if \
col.startswith('phone_applications_foreground_rapids_') or
col.startswith('phone_battery_rapids_') or
col.startswith('phone_bluetooth_rapids_') or
col.startswith('phone_light_rapids_') or
col.startswith('phone_calls_rapids_') or
col.startswith('phone_messages_rapids_') or
col.startswith('phone_screen_rapids_') or
col.startswith('phone_wifi_visible')]
features[impute_locations] = impute(features[impute_locations], method="zero")
## (5) 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[impute_locations], "knn_before")
# (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
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[impute_locations], "knn_after")
# (7) REMOVE COLS WHERE VARIANCE IS 0
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
@ -140,4 +155,3 @@ def graph_bf_af(features, phase_name):
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
plt.savefig(f'features_individual_nans_{phase_name}.png', bbox_inches='tight')

23
src/features/all_cleaning_overall/straw/main.py
View File

@ -1,7 +1,6 @@
import pandas as pd
import numpy as np
import math, sys, random
import typing
import yaml
from sklearn.impute import KNNImputer
@ -95,16 +94,16 @@ def straw_cleaning(sensor_data_files, provider):
# # features[impute_locations] = features[impute_locations].mask(np.random.random(features[impute_locations].shape) < .1)
# # features.at[0,'pid'] = "p01"
# # features.at[1,'pid'] = "p01"
# # features.at[2,'pid'] = "p02"
# # features.at[3,'pid'] = "p02"
# features.at[0,'pid'] = "p01"
# features.at[1,'pid'] = "p01"
# features.at[2,'pid'] = "p02"
# features.at[3,'pid'] = "p02"
# # graph_bf_af(features[impute_locations], "phoneloc_before")
# 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]
## (5) STANDARDIZATION
# (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())
@ -171,13 +170,3 @@ def graph_bf_af(features, phase_name):
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
class SklearnWrapper:
def __init__(self, transform: typing.Callable):
self.transform = transform
def __call__(self, df):
transformed = self.transform.fit_transform(df.values)
return pd.DataFrame(transformed, columns=df.columns, index=df.index)