Revert "Add Speech sensor - preparation."

This reverts commit 74fd4dfbd7.

Snakefile

@@ -174,14 +174,6 @@ for provider in config["PHONE_ESM"]["PROVIDERS"].keys():
         # files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
         # files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
 
-for provider in config["PHONE_SPEECH"]["PROVIDERS"].keys():
-    if config["PHONE_SPEECH"]["PROVIDERS"][provider]["COMPUTE"]:
-        files_to_compute.extend(expand("data/raw/{pid}/phone_speech_raw.csv",pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/raw/{pid}/phone_speech_with_datetime.csv",pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/interim/{pid}/phone_speech_clean.csv",pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/interim/{pid}/phone_speech_features/phone_speech_{language}_{provider_key}.csv",pid=config["PIDS"],language=get_script_language(config["PHONE_SPEECH"]["PROVIDERS"][provider]["SRC_SCRIPT"]),provider_key=provider.lower()))
-        files_to_compute.extend(expand("data/processed/features/{pid}/phone_speech.csv", pid=config["PIDS"]))
-
 # We can delete these if's as soon as we add feature PROVIDERS to any of these sensors
 if isinstance(config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"], dict):
     for provider in config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"].keys():

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: ['p03'] #['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']
+PIDS: ['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -248,15 +248,6 @@ PHONE_ESM:
       FEATURES: [mean]
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
-# Custom sensor
-PHONE_SPEECH:
-  CONTAINER: speech
-  PROVIDERS:
-    RAPIDS:
-      COMPUTE: True
-      FEATURES: ["countscans"]
-      SRC_SCRIPT: src/features/phone_speech/straw/main.py
-
 # See https://www.rapids.science/latest/features/phone-keyboard/
 PHONE_KEYBOARD:
   CONTAINER: keyboard
@@ -358,7 +349,6 @@ PHONE_WIFI_VISIBLE:
 
 
 
-
 ########################################################################################################################
 #                                                 FITBIT                                                               #
 ########################################################################################################################

rules/features.smk

@@ -345,19 +345,6 @@ rule esm_features:
     script:
         "../src/features/entry.py"
 
-rule phone_speech_python_features:
-    input:
-        sensor_data = "data/interim/{pid}/phone_speech_with_datetime.csv",
-        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
-    params:
-        provider = lambda wildcards: config["PHONE_SPEECH"]["PROVIDERS"][wildcards.provider_key.upper()],
-        provider_key = "{provider_key}",
-        sensor_key = "phone_speech",
-    output: 
-        "data/interim/{pid}/phone_speech_features/phone_speech_python_{provider_key}.csv"
-    script:
-        "../src/features/entry.py"
-
 rule phone_keyboard_python_features:
     input:
         sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",

src/features/phone_speech/straw/main.py

@@ -1,67 +0,0 @@
-import pandas as pd
-
-# TODO: adjust features files
-QUESTIONNAIRE_IDS = {
-    "sleep_quality": 1,
-    "PANAS_positive_affect": 8,
-    "PANAS_negative_affect": 9,
-    "JCQ_job_demand": 10,
-    "JCQ_job_control": 11,
-    "JCQ_supervisor_support": 12,
-    "JCQ_coworker_support": 13,
-    "PFITS_supervisor": 14,
-    "PFITS_coworkers": 15,
-    "UWES_vigor": 16,
-    "UWES_dedication": 17,
-    "UWES_absorption": 18,
-    "COPE_active": 19,
-    "COPE_support": 20,
-    "COPE_emotions": 21,
-    "balance_life_work": 22,
-    "balance_work_life": 23,
-    "recovery_experience_detachment": 24,
-    "recovery_experience_relaxation": 25,
-    "symptoms": 26,
-    "appraisal_stressfulness_event": 87,
-    "appraisal_threat": 88,
-    "appraisal_challenge": 89,
-    "appraisal_event_time": 90,
-    "appraisal_event_duration": 91,
-    "appraisal_event_work_related": 92,
-    "appraisal_stressfulness_period": 93,
-    "late_work": 94,
-    "work_hours": 95,
-    "left_work": 96,
-    "activities": 97,
-    "coffee_breaks": 98,
-    "at_work_yet": 99,
-}
-
-
-def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    esm_data = pd.read_csv(sensor_data_files["sensor_data"])
-    requested_features = provider["FEATURES"]
-    # name of the features this function can compute
-    requested_scales = provider["SCALES"]
-    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
-                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
-    #TODO Check valid questionnaire and feature names.
-    # the subset of requested features this function can compute
-    features_to_compute = list(set(requested_features) & set(base_features_names))
-    esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
-    if not esm_data.empty:
-        esm_data = filter_data_by_segment(esm_data, time_segment)
-
-        if not esm_data.empty:
-            esm_features = pd.DataFrame()
-            for scale in requested_scales:
-                questionnaire_id = QUESTIONNAIRE_IDS[scale]
-                mask = esm_data["questionnaire_id"] == questionnaire_id
-                esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
-                #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
-
-            esm_features = esm_features.reset_index()
-            if 'index' in esm_features: # In calse of empty esm_features df 
-                esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
-
-    return esm_features