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Make screen_features.py script compatible with both Pandas 0.25.2 and 1.0.3.

pull/95/head
Meng Li 2020-04-30 10:49:49 -04:00
parent 7cbb227214
commit 2720b24c0a
1 changed files with 7 additions and 7 deletions
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14
src/features/screen_features.py
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@ -9,22 +9,22 @@ def getEpisodeDurationFeatures(screen_deltas, episode, features, phone_sensed_bi
screen_deltas_episode = screen_deltas[screen_deltas["episode"] == episode]
duration_helper = pd.DataFrame()
if "countepisode" in features:
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).count()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1)
duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).count().rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1)
if "episodepersensedminutes" in features:
for date, row in screen_deltas_episode.groupby(["local_start_date"]).count()[["time_diff"]].iterrows():
for date, row in screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).count().iterrows():
sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size
episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes)
duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes
if "sumduration" in features:
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).sum()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1)
duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).sum().rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1)
if "maxduration" in features:
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).max()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1)
duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).max().rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1)
if "minduration" in features:
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).min()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1)
duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).min().rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1)
if "avgduration" in features:
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).mean()[["time_diff"]].rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1)
duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).mean().rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1)
if "stdduration" in features:
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).std()[["time_diff"]].rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1)
duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).std().rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1)
if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_deltas_episode.groupby(["local_start_date"]).first()[["local_start_date_time"]].local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 3600 + x.to_pydatetime().minute * 60 + x.to_pydatetime().second)).rename(columns = {"local_start_date_time":"screen_" + day_segment + "_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1)
return duration_helper