115 lines
8.4 KiB
Python
115 lines
8.4 KiB
Python
import pandas as pd
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import itertools
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def extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features, sleep_type, sleep_summary_features):
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if sleep_type == "main":
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sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1]
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elif sleep_type == "nap":
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sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0]
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elif sleep_type == "all":
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pass
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else:
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raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].")
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features_sum = sleep_summary_data[["local_segment", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).sum()
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if "sumdurationafterwakeup" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sumdurationafterwakeup" + sleep_type})
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if "sumdurationasleep" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sumdurationasleep" + sleep_type})
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if "sumdurationawake" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sumdurationawake" + sleep_type})
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if "sumdurationtofallasleep" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sumdurationtofallasleep" + sleep_type})
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if "sumdurationinbed" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sumdurationinbed" + sleep_type})
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features_avg = sleep_summary_data[["local_segment", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).mean()
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if "avgefficiency" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "avgefficiency" + sleep_type})
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if "avgdurationafterwakeup" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "avgdurationafterwakeup" + sleep_type})
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if "avgdurationasleep" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "avgdurationasleep" + sleep_type})
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if "avgdurationawake" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "avgdurationawake" + sleep_type})
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if "avgdurationtofallasleep" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "avgdurationtofallasleep" + sleep_type})
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if "avgdurationinbed" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "avgdurationinbed" + sleep_type})
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features_count = sleep_summary_data[["local_segment", "timestamp"]].groupby(["local_segment"]).count()
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if "countepisode" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "countepisode" + sleep_type})
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features_first = sleep_summary_data[["local_segment", "minutes_start_episode", "minutes_end_episode"]].groupby(["local_segment"]).first()
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if "firstwaketime" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_first[["minutes_end_episode"]].shift(), how="outer").rename(columns={"minutes_end_episode": "firstwaketime" + sleep_type})
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if "firstbedtime" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_first[["minutes_start_episode"]], how="outer").rename(columns={"minutes_start_episode": "firstbedtime" + sleep_type})
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features_last = sleep_summary_data[["local_segment", "minutes_start_episode", "minutes_end_episode"]].groupby(["local_segment"]).last()
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if "lastwaketime" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_last[["minutes_end_episode"]].shift(), how="outer").rename(columns={"minutes_end_episode": "lastwaketime" + sleep_type})
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if "lastbedtime" in summary_features:
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sleep_summary_features = sleep_summary_features.join(features_last[["minutes_start_episode"]], how="outer").rename(columns={"minutes_start_episode": "lastbedtime" + sleep_type})
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return sleep_summary_features
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def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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sleep_summary_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_summary_features = provider["FEATURES"]
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requested_sleep_types = provider["SLEEP_TYPES"]
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# name of the features this function can compute
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base_summary_features = ["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
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base_sleep_types = ["main", "nap", "all"]
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# the subset of requested features this function can compute
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summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features))
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sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types))
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# full names
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features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_types_to_compute)]
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colnames_can_be_zero = ["".join(feature) for feature in itertools.product(set(summary_features_to_compute) - set(["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "avgefficiency"]), sleep_types_to_compute)]
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# extract features from summary data
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sleep_summary_features = pd.DataFrame(columns=["local_segment"] + features_fullnames_to_compute)
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if not sleep_summary_data.empty:
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sleep_summary_data = filter_data_by_segment(sleep_summary_data, time_segment)
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notna_segments = sleep_summary_data[sleep_summary_data["type"].notna()]["local_segment"].unique()
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if not sleep_summary_data.empty:
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# only keep the segments start at 00:00:00 and end at 23:59:59
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datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
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datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
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segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex)
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sleep_summary_data = sleep_summary_data[sleep_summary_data["local_segment"].str.match(segment_regex)]
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# calculate number of minutes after segment's start date time
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dt_cols = ["local_start_date_time", "local_end_date_time", "local_date_time"]
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sleep_summary_data[dt_cols] = sleep_summary_data[dt_cols].apply(pd.to_datetime)
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sleep_summary_data["minutes_start_episode"] = (sleep_summary_data["local_start_date_time"] - sleep_summary_data["local_date_time"]) / pd.Timedelta(minutes=1)
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sleep_summary_data["minutes_end_episode"] = (sleep_summary_data["local_end_date_time"] - (sleep_summary_data["local_date_time"] + pd.Timedelta(days=1))) / pd.Timedelta(minutes=1)
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if not sleep_summary_data.empty:
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sleep_summary_features = pd.DataFrame()
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for sleep_type in sleep_types_to_compute:
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sleep_summary_features = extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features_to_compute, sleep_type, sleep_summary_features)
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sleep_summary_features.loc[notna_segments, colnames_can_be_zero] = sleep_summary_features.loc[notna_segments, colnames_can_be_zero].fillna(0)
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sleep_summary_features = sleep_summary_features.reset_index()
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return sleep_summary_features
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