Add a function to fix SAM question IDs.
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ef26772038
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@ -3,6 +3,9 @@ import pandas as pd
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import features.esm
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import features.esm
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SAM_ORIGINAL_MAX = 5
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SAM_ORIGINAL_MIN = 1
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QUESTIONNAIRE_ID_SAM = {
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QUESTIONNAIRE_ID_SAM = {
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"event_stress": 87,
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"event_stress": 87,
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"event_threat": 88,
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"event_threat": 88,
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@ -441,3 +444,58 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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# TODO: How many questions about the stressfulness of the period were asked
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# TODO: How many questions about the stressfulness of the period were asked
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# and how does this relate to events?
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# and how does this relate to events?
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def reassign_question_ids(df_sam_cleaned: pd.DataFrame) -> pd.DataFrame:
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df_esm_sam_unique_questions = (
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df_sam_cleaned.groupby("question_id")
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.esm_instructions.value_counts()
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.rename()
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.reset_index()
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)
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# Tabulate all possible answers to each question (group by question ID).
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# First, check that we anticipated all esm instructions.
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for q_id in DICT_SAM_QUESTION_IDS.keys():
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# Look for all questions ("instructions") occurring in the dataframe.
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actual_questions = df_esm_sam_unique_questions.loc[
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df_esm_sam_unique_questions["question_id"] == q_id,
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"esm_instructions",
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]
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# These are all answers to a given question (by q_id).
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questions_matches = actual_questions.str.startswith(
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DICT_SAM_QUESTION_IDS.get(q_id)
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)
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# See if they are expected, i.e. included in the dictionary.
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if ~actual_questions.all():
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print("One of the questions that occur in the data was undefined.")
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print("This were the questions found in the data: ")
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raise KeyError(actual_questions[~questions_matches])
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# In case there is an unexpected answer, raise an exception.
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# Next, replace question IDs.
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df_sam_fixed = df_sam_cleaned.copy()
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df_sam_fixed["question_id"] = df_sam_cleaned["esm_instructions"].apply(
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lambda x: next(
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(
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key
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for key, values in DICT_SAM_QUESTION_IDS.items()
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if x.startswith(values)
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),
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None,
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)
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)
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# Finally, increment numeric answers.
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try:
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df_sam_fixed = df_sam_fixed.assign(
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esm_user_score=lambda x: x.esm_user_answer_numeric + 1
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)
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# Increment the original answer by 1
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# to keep in line with traditional scoring
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# (from SAM_ORIGINAL_MIN - SAM_ORIGINAL_MAX).
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except AttributeError as e:
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print("Please, clean the dataframe first using features.esm.clean_up_esm.")
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print(e)
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return df_sam_fixed
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