136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
import datetime
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import warnings
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from pathlib import Path
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from typing import Collection
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import pandas as pd
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from pyprojroot import here
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import participants.query_db
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from features import esm
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from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
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from machine_learning.helper import read_csv_with_settings, to_csv_with_settings
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WARNING_PARTICIPANTS_LABEL = (
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"Before aggregating labels, please set participants label using self.set_participants_label() "
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"to be used as a filename prefix when exporting data. "
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"The filename will be of the form: %participants_label_%grouping_variable_%data_type.csv"
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)
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class Labels:
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def __init__(
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self,
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grouping_variable: str,
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labels: dict,
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participants_usernames: Collection = None,
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) -> None:
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self.grouping_variable_name = grouping_variable
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self.grouping_variable = [grouping_variable]
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self.questionnaires = labels.keys()
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self.participants_label: str = ""
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if participants_usernames is None:
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participants_usernames = participants.query_db.get_usernames(
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collection_start=datetime.date.fromisoformat("2020-08-01")
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)
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self.participants_label = "all"
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self.participants_usernames = participants_usernames
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self.df_esm = pd.DataFrame()
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self.df_esm_preprocessed = pd.DataFrame()
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self.df_esm_interest = pd.DataFrame()
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self.df_esm_clean = pd.DataFrame()
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self.df_esm_means = pd.DataFrame()
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self.folder: Path = Path()
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self.filename_prefix = ""
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self.construct_export_path()
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print("Labels initialized.")
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def set_labels(self) -> None:
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print("Querying database ...")
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self.df_esm = esm.get_esm_data(self.participants_usernames)
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print("Got ESM data from the DB.")
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self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
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print("ESM data preprocessed.")
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if "PANAS" in self.questionnaires:
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self.df_esm_interest = self.df_esm_preprocessed[
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(
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self.df_esm_preprocessed["questionnaire_id"]
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== QUESTIONNAIRE_IDS.get("PANAS").get("PA")
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)
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| (
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self.df_esm_preprocessed["questionnaire_id"]
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== QUESTIONNAIRE_IDS.get("PANAS").get("NA")
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)
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]
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self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
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print("ESM data cleaned.")
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def get_labels(self, questionnaire: str) -> pd.DataFrame:
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if questionnaire == "PANAS":
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return self.df_esm_clean
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else:
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raise KeyError("This questionnaire has not been implemented as a label.")
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def aggregate_labels(self, cached=True) -> None:
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print("Aggregating labels ...")
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if not self.participants_label:
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raise ValueError(WARNING_PARTICIPANTS_LABEL)
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try:
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if not cached: # Do not use the file, even if it exists.
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raise FileNotFoundError
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self.df_esm_means = read_csv_with_settings(
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self.folder,
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self.filename_prefix,
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data_type="_".join(self.questionnaires),
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grouping_variable=self.grouping_variable,
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)
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print("Read labels from the file.")
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except FileNotFoundError:
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# We need to recalculate the features in this case.
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self.df_esm_means = (
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self.df_esm_clean.groupby(
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["participant_id", "questionnaire_id"] + self.grouping_variable
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)
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.esm_user_answer_numeric.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
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)
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self.df_esm_means = (
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self.df_esm_means.pivot(
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index=["participant_id"] + self.grouping_variable,
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columns="questionnaire_id",
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values="esm_numeric_mean",
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)
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.reset_index(col_level=1)
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.rename(columns=QUESTIONNAIRE_IDS_RENAME)
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.set_index(["participant_id"] + self.grouping_variable)
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)
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print("Labels aggregated.")
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to_csv_with_settings(
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self.df_esm_means,
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self.folder,
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self.filename_prefix,
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data_type="_".join(self.questionnaires),
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)
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def get_aggregated_labels(self) -> pd.DataFrame:
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return self.df_esm_means
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def construct_export_path(self) -> None:
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if not self.participants_label:
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warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
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self.folder = here("machine_learning/intermediate_results/labels", warn=True)
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self.filename_prefix = (
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self.participants_label + "_" + self.grouping_variable_name
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)
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def set_participants_label(self, label: str) -> None:
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self.participants_label = label
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self.construct_export_path()
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