From 90ee99e4b99eeb32650df06d60e43c0ee1e66a87 Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 21 Sep 2022 07:16:00 +0000 Subject: [PATCH] Remove TODO comments --- src/features/phone_bluetooth/doryab/main.py | 3 +-- src/features/phone_locations/doryab/main.py | 1 - 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/src/features/phone_bluetooth/doryab/main.py b/src/features/phone_bluetooth/doryab/main.py index f83fc457..bbc7a47c 100644 --- a/src/features/phone_bluetooth/doryab/main.py +++ b/src/features/phone_bluetooth/doryab/main.py @@ -14,8 +14,7 @@ def deviceFeatures(devices, ownership, common_devices, features_to_compute, feat features = features.join(device_value_counts.groupby("local_segment")["bt_address"].nunique().to_frame("uniquedevices" + ownership), how="outer") if "meanscans" in features_to_compute: features = features.join(device_value_counts.groupby("local_segment")["scans"].mean().to_frame("meanscans" + ownership), how="outer") - if "stdscans" in features_to_compute: - # TODO: check if std scans implementation works + if "stdscans" in features_to_compute: features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership).fillna(0), how="outer") # Most frequent device within segments, across segments, and across dataset if "countscansmostfrequentdevicewithinsegments" in features_to_compute: diff --git a/src/features/phone_locations/doryab/main.py b/src/features/phone_locations/doryab/main.py index 6be0d1ad..e4dc0117 100644 --- a/src/features/phone_locations/doryab/main.py +++ b/src/features/phone_locations/doryab/main.py @@ -38,7 +38,6 @@ def variance_and_logvariance_features(location_data, location_features): location_features["locationvariance"] = ((location_data_grouped["latitude_for_wvar"].sum() + location_data_grouped["longitude_for_wvar"].sum()) / (location_data_grouped["duration"].sum() * 60 - 1)).fillna(0) - # TODO: loglocationvariance - check if the implementation works location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, -1000000) return location_features