create features_utils.py with epoch split function
Meng Li
parent
2230421167
commit
9eaa48c7dd
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@ -1,90 +1,18 @@
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import pandas as pd
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from datetime import datetime, timedelta, time
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def truncateTime(df, segment_column, new_day_segment, datetime_column, date_column, new_time):
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df.loc[:, segment_column] = new_day_segment
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df.loc[:, datetime_column] = df[date_column].apply(lambda date: datetime.combine(date, new_time))
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return df
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def computeTruncatedBatteryTimeDifferences(df):
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df["truncated_time_diff"] = df["local_end_date_time"] - df["local_start_date_time"]
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df["truncated_time_diff"] = df["truncated_time_diff"].apply(lambda time: time.total_seconds()/3600)
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df["battery_diff"] = df["battery_diff"] * (df["truncated_time_diff"] / df["time_diff"])
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del df["time_diff"]
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df.rename(columns={"truncated_time_diff": "time_diff"}, inplace=True)
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return df
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def splitOvernightEpisodes(battery_data):
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overnight = battery_data[(battery_data["local_start_date"] + timedelta(days=1)) == battery_data["local_end_date"]]
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not_overnight = battery_data[battery_data["local_start_date"] == battery_data["local_end_date"]]
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if not overnight.empty:
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today = overnight[["battery_diff", "time_diff", "local_start_date_time", "local_start_date", "local_start_day_segment"]].copy()
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tomorrow = overnight[["battery_diff", "time_diff", "local_end_date_time", "local_end_date", "local_end_day_segment"]].copy()
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# truncate the end time of all overnight periods to midnight
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today = truncateTime(today, "local_end_day_segment", "evening", "local_end_date_time", "local_start_date", time(23,59,59))
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today["local_end_date"] = overnight["local_start_date"]
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# set the start time of all periods after midnight to midnight
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tomorrow = truncateTime(tomorrow, "local_start_day_segment", "night", "local_start_date_time", "local_end_date", time(0,0,0))
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tomorrow["local_start_date"] = overnight["local_end_date"]
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overnight = pd.concat([today, tomorrow], axis=0, sort=False)
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# calculate new battery_diff and time_diff for split overnight periods
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overnight = computeTruncatedBatteryTimeDifferences(overnight)
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return pd.concat([not_overnight, overnight], axis=0, sort=False)
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def splitMultiSegmentEpisodes(battery_data, day_segment):
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# extract episodes that start and end at the same epochs
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exact_segments = battery_data.query("local_start_day_segment == local_end_day_segment and local_start_day_segment == @day_segment").copy()
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# extract episodes that start and end at different epochs
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across_segments = battery_data.query("local_start_day_segment != local_end_day_segment").copy()
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# 1) if start time is in current day_segment
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start_segment = across_segments[across_segments["local_start_day_segment"] == day_segment].copy()
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if not start_segment.empty:
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start_segment = truncateTime(start_segment, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59))
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# 2) if end time is in current day_segment
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end_segment = across_segments[across_segments["local_end_day_segment"] == day_segment].copy()
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if not end_segment.empty:
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end_segment = truncateTime(end_segment, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0))
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# 3) if current episode comtains day_segment
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across_segments.loc[:,"start_segment"] = across_segments["local_start_day_segment"].apply(lambda seg: SEGMENT[seg])
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across_segments.loc[:,"end_segment"] = across_segments["local_end_day_segment"].apply(lambda seg: SEGMENT[seg])
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day_segment_num = SEGMENT[day_segment]
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within_segments = across_segments.query("start_segment < @day_segment_num and end_segment > @day_segment_num")
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del across_segments["start_segment"], across_segments["end_segment"]
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del within_segments["start_segment"], within_segments["end_segment"]
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if not within_segments.empty:
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within_segments = truncateTime(within_segments, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0))
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within_segments = truncateTime(within_segments, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59))
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across_segments = pd.concat([start_segment, end_segment, within_segments], axis=0, sort=False)
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if not across_segments.empty:
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accross_segments = computeTruncatedBatteryTimeDifferences(across_segments)
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return pd.concat([exact_segments, across_segments], axis=0, sort=False)
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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battery_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
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day_segment = snakemake.params["day_segment"]
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metrics = snakemake.params["metrics"]
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SEGMENT = {"night": 0, "morning": 1, "afternoon": 2, "evening": 3}
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EPOCH_TIMES = {"night": [0,5], "morning": [6,11], "afternoon": [12,17], "evening": [18,23]}
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if battery_data.empty:
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battery_features = pd.DataFrame(columns=["local_date"] + ["battery_" + day_segment + "_" + x for x in metrics])
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else:
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battery_data = splitOvernightEpisodes(battery_data)
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battery_data = splitOvernightEpisodes(battery_data, ["battery_diff"])
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if day_segment != "daily":
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battery_data = splitMultiSegmentEpisodes(battery_data, day_segment)
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battery_data = splitMultiSegmentEpisodes(battery_data, day_segment, ["battery_diff"])
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battery_data["battery_consumption_rate"] = battery_data["battery_diff"] / battery_data["time_diff"]
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@ -0,0 +1,78 @@
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import pandas as pd
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from datetime import datetime, timedelta, time
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SEGMENT = {"night": 0, "morning": 1, "afternoon": 2, "evening": 3}
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EPOCH_TIMES = {"night": [0,5], "morning": [6,11], "afternoon": [12,17], "evening": [18,23]}
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def truncateTime(df, segment_column, new_day_segment, datetime_column, date_column, new_time):
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df.loc[:, segment_column] = new_day_segment
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df.loc[:, datetime_column] = df[date_column].apply(lambda date: datetime.combine(date, new_time))
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return df
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# calculate truncated time differences and truncated extra_cols if it is not empty
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def computeTruncatedDifferences(df, extra_cols):
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df["truncated_time_diff"] = df["local_end_date_time"] - df["local_start_date_time"]
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df["truncated_time_diff"] = df["truncated_time_diff"].apply(lambda time: time.total_seconds()/3600)
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if extra_cols:
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for extra_col in extra_cols:
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df[extra_col] = df[extra_col] * (df["truncated_time_diff"] / df["time_diff"])
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del df["time_diff"]
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df.rename(columns={"truncated_time_diff": "time_diff"}, inplace=True)
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return df
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def splitOvernightEpisodes(sensor_deltas, extra_cols):
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overnight = sensor_deltas[(sensor_deltas["local_start_date"] + timedelta(days=1)) == sensor_deltas["local_end_date"]]
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not_overnight = sensor_deltas[sensor_deltas["local_start_date"] == sensor_deltas["local_end_date"]]
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if not overnight.empty:
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today = overnight[extra_cols + ["time_diff", "local_start_date_time", "local_start_date", "local_start_day_segment"]].copy()
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tomorrow = overnight[extra_cols + ["time_diff", "local_end_date_time", "local_end_date", "local_end_day_segment"]].copy()
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# truncate the end time of all overnight periods to midnight
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today = truncateTime(today, "local_end_day_segment", "evening", "local_end_date_time", "local_start_date", time(23,59,59))
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today["local_end_date"] = overnight["local_start_date"]
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# set the start time of all periods after midnight to midnight
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tomorrow = truncateTime(tomorrow, "local_start_day_segment", "night", "local_start_date_time", "local_end_date", time(0,0,0))
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tomorrow["local_start_date"] = overnight["local_end_date"]
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overnight = pd.concat([today, tomorrow], axis=0, sort=False)
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# calculate new time_diff and extra_cols for split overnight periods
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overnight = computeTruncatedDifferences(overnight, extra_cols)
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return pd.concat([not_overnight, overnight], axis=0, sort=False)
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def splitMultiSegmentEpisodes(sensor_deltas, day_segment, extra_cols):
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# extract episodes that start and end at the same epochs
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exact_segments = sensor_deltas.query("local_start_day_segment == local_end_day_segment and local_start_day_segment == @day_segment").copy()
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# extract episodes that start and end at different epochs
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across_segments = sensor_deltas.query("local_start_day_segment != local_end_day_segment").copy()
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# 1) if start time is in current day_segment
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start_segment = across_segments[across_segments["local_start_day_segment"] == day_segment].copy()
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if not start_segment.empty:
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start_segment = truncateTime(start_segment, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59))
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# 2) if end time is in current day_segment
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end_segment = across_segments[across_segments["local_end_day_segment"] == day_segment].copy()
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if not end_segment.empty:
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end_segment = truncateTime(end_segment, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0))
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# 3) if current episode comtains day_segment
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across_segments.loc[:,"start_segment"] = across_segments["local_start_day_segment"].apply(lambda seg: SEGMENT[seg])
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across_segments.loc[:,"end_segment"] = across_segments["local_end_day_segment"].apply(lambda seg: SEGMENT[seg])
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day_segment_num = SEGMENT[day_segment]
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within_segments = across_segments.query("start_segment < @day_segment_num and end_segment > @day_segment_num")
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del across_segments["start_segment"], across_segments["end_segment"]
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del within_segments["start_segment"], within_segments["end_segment"]
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if not within_segments.empty:
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within_segments = truncateTime(within_segments, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0))
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within_segments = truncateTime(within_segments, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59))
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across_segments = pd.concat([start_segment, end_segment, within_segments], axis=0, sort=False)
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if not across_segments.empty:
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accross_segments = computeTruncatedDifferences(across_segments, extra_cols)
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return pd.concat([exact_segments, across_segments], axis=0, sort=False)
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