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Replace NA with 0 for count features of apps sensor

pull/95/head
Meng Li 2020-03-09 14:04:51 -04:00
parent e106bb3190
commit 1c413838ad
1 changed files with 4 additions and 3 deletions
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7
src/features/applications_foreground_metrics.py
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@ -4,9 +4,7 @@ import itertools
from scipy.stats import entropy
def compute_metrics(filtered_data, apps_type, metrics, apps_features):
if "count" in metrics:
apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
def compute_metrics(filtered_data, apps_type, metrics, apps_features):
if "timeoffirstuse" in metrics:
time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
@ -16,6 +14,9 @@ def compute_metrics(filtered_data, apps_type, metrics, apps_features):
if "frequencyentropy" in metrics:
apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy)
if "count" in metrics:
apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
apps_features.fillna(value={"apps_" + day_segment + "_count" + apps_type: 0}, inplace=True)
return apps_features