108 lines
5.3 KiB
Python
108 lines
5.3 KiB
Python
import pandas as pd
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import sys
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import warnings
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sys.path.append('src/features/phone_esm/straw')
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from esm_activities import esm_activities_LTM_features,process_answers_aggregation
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QUESTIONNAIRE_IDS = {
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"sleep_quality": 1,
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"PANAS_positive_affect": 8,
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"PANAS_negative_affect": 9,
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"JCQ_job_demand": 10,
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"JCQ_job_control": 11,
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"JCQ_supervisor_support": 12,
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"JCQ_coworker_support": 13,
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"PFITS_supervisor": 14,
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"PFITS_coworkers": 15,
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"UWES_vigor": 16,
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"UWES_dedication": 17,
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"UWES_absorption": 18,
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"COPE_active": 19,
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"COPE_support": 20,
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"COPE_emotions": 21,
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"balance_life_work": 22,
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"balance_work_life": 23,
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"recovery_experience_detachment": 24,
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"recovery_experience_relaxation": 25,
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"symptoms": 26,
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"appraisal_stressfulness_event": 87,
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"appraisal_threat": 88,
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"appraisal_challenge": 89,
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"appraisal_event_time": 90,
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"appraisal_event_duration": 91,
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"appraisal_event_work_related": 92,
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"appraisal_stressfulness_period": 93,
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"late_work": 94,
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"work_hours": 95,
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"left_work": 96,
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"activities": 97,
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"coffee_breaks": 98,
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"at_work_yet": 99,
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}
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def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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esm_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_features = provider["FEATURES"]
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# name of the features this function can compute
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requested_scales = provider["SCALES"]
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base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
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"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge","activities_n_others","activities_inperson","activities_formal"]
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#TODO Check valid questionnaire and feature names.
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
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if not esm_data.empty:
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esm_data = filter_data_by_segment(esm_data, time_segment)
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if not esm_data.empty:
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esm_features = pd.DataFrame()
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for scale in requested_scales:
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questionnaire_id = QUESTIONNAIRE_IDS[scale]
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mask = esm_data["questionnaire_id"] == questionnaire_id
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#print(esm_data.loc[mask].head())
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#print(time_segment)
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if not mask.any():
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temp = sensor_data_files["sensor_data"]
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warnings.warn(f"Warning........... No relevant questions for scale {scale} in {temp}-{time_segment}",RuntimeWarning)
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continue
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#TODO: calculation of LTM features
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if scale=="activities":
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requested_subset = [req for req in requested_features if req.startswith("activities")]
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if not bool(requested_subset):
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continue
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# ltm_features = esm_activities_LTM_features(esm_data.loc[mask])
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# print(esm_data["esm_json"].values)
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# print(mask)
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# print(esm_data.loc[mask])
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# #ltm_features = ltm_features[ltm_features["correct_ids"==44]]
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#print(esm_data.loc[mask]["local_segment"])
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ltm_features = esm_data.loc[mask].groupby(["local_segment"]).apply(process_answers_aggregation)
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#print("PRINTING ltm_features:\n",ltm_features)
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ltm_features.rename(columns={"n_others":"activities_n_others","inperson":"activities_inperson","formal":"activities_formal"},inplace=True)
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esm_features[requested_subset] = ltm_features.groupby("local_segment").first()[requested_subset]
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#print(esm_features.columns)
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#print("PRINTING esm_features after rename:\n",ltm_features)
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#FIXME: it might be an issue that im calculating for whole time segment and not grouping by "local segment"
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#print("~~~~~~~~~~~~~~~~~~~~~~~~===============================~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n LTM FEATURES STORED... AFTER RETURN:\n",ltm_features,esm_features[["activities_"+req for req in requested_subset]])
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if("mean" in features_to_compute):
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esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
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#TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
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esm_features = esm_features.reset_index()
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if 'index' in esm_features: # In calse of empty esm_features df
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esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
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return esm_features
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def test_main():
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import temp_help
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provider = {
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"FEATURES":["mean","activities_n_others","activities_inperson","activities_formal"],
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"SCALES":['activities']
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}
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sensor_data_files = {"sensor_data":"data/interim/p069/phone_esm_clean.csv"}
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s_feat = straw_features(sensor_data_files,"straw_event_stress_event_p069_110",provider,temp_help.filter_data_by_segment)
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print(s_feat)
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#test_main() |