Add logic for target selection in ERS processing.
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0ce8723bdb
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@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
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INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
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TAILORED_EVENTS: # Only relevant if TYPE=EVENT
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COMPUTE: True
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PARAMETER_ONE: "something"
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TARGETS_METHOD: "none"
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# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
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TIMEZONE:
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@ -28,17 +28,13 @@ def extract_ers_from_file(esm_df, device_id):
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pd.set_option("display.max_rows", None)
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pd.set_option("display.max_columns", None)
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# extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
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with open('config.yaml', 'r') as stream:
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config = yaml.load(stream, Loader=yaml.FullLoader)
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targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
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# esm_df = clean_up_esm(preprocess_esm(esm_df))
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esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
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# Take only during work sessions
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# during_work = esm_df[esm_df["esm_trigger"].str.contains("during_work", na=False)]
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# esm_trigger_group = esm_df.groupby("esm_session").agg(pd.Series.mode)['esm_trigger'] # Get most frequent esm_trigger within particular session
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# esm_filtered_sessions = list(esm_trigger_group[esm_trigger_group == 'during_work'].index) # Take only sessions that contains during work
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# Take only ema_completed sessions responses
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classified = classify_sessions_by_completion_time(esm_preprocessed)
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esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()['esm_session']
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